{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tiny TextGrad: A Minimal Text Gradient Descent Implementation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LLM Helpers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from textwrap import dedent\n",
    "\n",
    "from litellm import completion\n",
    "\n",
    "\n",
    "def _call_llm(\n",
    "  prompt: str,\n",
    "  input: str,\n",
    "  model: str = \"gpt-4o-mini\",\n",
    "  temperature: float = 0.9,\n",
    "  max_tokens: int = 4096,\n",
    "  top_p: float = 0.95,\n",
    "  frequency_penalty: float = 0,\n",
    "  format_as_json: bool = False,\n",
    ") -> str:\n",
    "  messages = [\n",
    "    {\"role\": \"system\", \"content\": prompt},\n",
    "    {\"role\": \"user\", \"content\": input},\n",
    "  ]\n",
    "  response = completion(\n",
    "    model=model,\n",
    "    messages=messages,\n",
    "    temperature=temperature,\n",
    "    max_tokens=max_tokens,\n",
    "    top_p=top_p,\n",
    "    frequency_penalty=frequency_penalty,\n",
    "    response_format={\n",
    "      \"type\": \"json_object\"\n",
    "      if format_as_json\n",
    "      else \"text\"\n",
    "    },\n",
    "  )\n",
    "  return response.choices[0].message.content\n",
    "\n",
    "\n",
    "def call_llm(\n",
    "  prompt: str,\n",
    "  input: str,\n",
    "  model: str = \"gpt-4o-mini\",\n",
    "  temperature: float = 0.9,\n",
    "  max_tokens: int = 4096,\n",
    "  top_p: float = 0.95,\n",
    "  frequency_penalty: float = 0,\n",
    ") -> str:\n",
    "  return _call_llm(\n",
    "    prompt=prompt,\n",
    "    input=input,\n",
    "    model=model,\n",
    "    temperature=temperature,\n",
    "    max_tokens=max_tokens,\n",
    "    top_p=top_p,\n",
    "    frequency_penalty=frequency_penalty,\n",
    "    format_as_json=False,\n",
    "  )\n",
    "\n",
    "\n",
    "def enforce_json(\n",
    "  json_prompt: str,\n",
    "  input: str,\n",
    "  model: str = \"gpt-4o-mini\",\n",
    "  temperature: float = 0.0,\n",
    "  max_tokens: int = 4096,\n",
    "  top_p: float = 0.95,\n",
    "  frequency_penalty: float = 0,\n",
    ") -> str:\n",
    "  return _call_llm(\n",
    "    prompt=json_prompt,\n",
    "    input=input,\n",
    "    model=model,\n",
    "    temperature=temperature,\n",
    "    max_tokens=max_tokens,\n",
    "    top_p=top_p,\n",
    "    frequency_penalty=frequency_penalty,\n",
    "    format_as_json=True,\n",
    "  )\n",
    "\n",
    "\n",
    "def call_llm_with_json_output(\n",
    "  prompt: str,\n",
    "  json_prompt: str,\n",
    "  input: str,\n",
    "  model: str = \"gpt-4o-mini\",\n",
    "  temperature: float = 0.9,\n",
    "  max_tokens: int = 4096,\n",
    "  json_max_tokens: int = 4096,\n",
    "  top_p: float = 0.95,\n",
    "  frequency_penalty: float = 0,\n",
    ") -> str:\n",
    "  result = call_llm(\n",
    "    prompt=prompt,\n",
    "    input=input,\n",
    "    model=model,\n",
    "    temperature=temperature,\n",
    "    max_tokens=max_tokens,\n",
    "    top_p=top_p,\n",
    "    frequency_penalty=frequency_penalty,\n",
    "  )\n",
    "  print(f\"Initial result: {result}\")\n",
    "  return enforce_json(\n",
    "    json_prompt=json_prompt,\n",
    "    input=result,\n",
    "    model=model,\n",
    "    temperature=0.0,\n",
    "    max_tokens=json_max_tokens,\n",
    "    top_p=top_p,\n",
    "    frequency_penalty=frequency_penalty,\n",
    "  )\n",
    "\n",
    "\n",
    "def get_json_list(json_data: dict) -> list:\n",
    "  key = next(iter(json_data))\n",
    "  if not isinstance(json_data[key], list):\n",
    "    raise ValueError(\n",
    "      \"The JSON data does not contain a list.\"\n",
    "    )\n",
    "  return json_data[key]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tiny TextGrad\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "from typing import Tuple\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class OptimizationResult:\n",
    "  optimized_prompt: str\n",
    "  model: str\n",
    "  temperature: float\n",
    "  top_p: float\n",
    "  frequency_penalty: float\n",
    "\n",
    "\n",
    "class Variable:\n",
    "  def __init__(\n",
    "    self,\n",
    "    value,\n",
    "    requires_grad=True,\n",
    "    role_description=\"\",\n",
    "  ) -> None:\n",
    "    self.value = value\n",
    "    self.requires_grad = requires_grad\n",
    "    self.role_description = role_description\n",
    "    self.grad = None\n",
    "\n",
    "  def set_gradient(self, grad: str):\n",
    "    if self.requires_grad:\n",
    "      self.grad = grad\n",
    "\n",
    "  def backward(\n",
    "    self, application_prompt: str, engine\n",
    "  ) -> None:\n",
    "    \"\"\"\n",
    "    Applies the gradient to the variable value using an engine.\n",
    "    \"\"\"\n",
    "    if self.requires_grad and self.grad:\n",
    "      new_value = engine.generate(\n",
    "        prompt=application_prompt,\n",
    "        input=f\"Original Prompt: {self.value}\\n\\nFeedback: {self.grad}\",\n",
    "      )\n",
    "      self.value = self._clean_prompt(\n",
    "        new_value, engine\n",
    "      ).strip()\n",
    "      print(f\"Updated value: {self.value}\")\n",
    "\n",
    "  def _clean_prompt(self, prompt, engine):\n",
    "    cleaned_prompt = engine.generate(\n",
    "      prompt=dedent(\"\"\"\n",
    "            Clean up the following prompt. Remove any meta-commentary or\n",
    "            explanations about the prompt itself. The result should be a clear,\n",
    "            concise prompt ready for direct use.\n",
    "            \"\"\"),\n",
    "      input=f\"Original Prompt: {prompt}\",\n",
    "    ).strip()\n",
    "    print(f\"Cleaned prompt: {cleaned_prompt}\")\n",
    "    return cleaned_prompt.strip()\n",
    "\n",
    "\n",
    "class Engine:\n",
    "  def __init__(\n",
    "    self,\n",
    "    model_name=\"gpt-4o-mini\",\n",
    "    temperature=0.7,\n",
    "    max_tokens=2048,\n",
    "    top_p=0.95,\n",
    "    frequency_penalty=0,\n",
    "  ):\n",
    "    self.model_name = model_name\n",
    "    self.temperature = temperature\n",
    "    self.max_tokens = max_tokens\n",
    "    self.top_p = top_p\n",
    "    self.frequency_penalty = frequency_penalty\n",
    "\n",
    "  def generate(self, prompt: str, input: str):\n",
    "    response = call_llm(\n",
    "      prompt=prompt,\n",
    "      input=input,\n",
    "      model=self.model_name,\n",
    "      temperature=self.temperature,\n",
    "      max_tokens=self.max_tokens,\n",
    "      top_p=self.top_p,\n",
    "      frequency_penalty=self.frequency_penalty,\n",
    "    )\n",
    "    return response\n",
    "\n",
    "\n",
    "class TextLoss:\n",
    "  def __init__(\n",
    "    self, feedback_prompt: str, engine: Engine\n",
    "  ):\n",
    "    self.feedback_prompt = feedback_prompt\n",
    "    self.engine = engine\n",
    "\n",
    "  def forward(\n",
    "    self,\n",
    "    prompt: str,\n",
    "    results: list[Tuple[str, str]],\n",
    "  ):\n",
    "    formatted_results = \"\\n\".join(\n",
    "      [\n",
    "        f\"Input: {input}\\nOutput: {output}\"\n",
    "        for input, output in results\n",
    "      ]\n",
    "    )\n",
    "    evaluation_input = f\"Prompt:\\n{prompt}\\n\\nResults:\\n{formatted_results}\"\n",
    "    feedback = self.engine.generate(\n",
    "      self.feedback_prompt, evaluation_input\n",
    "    )\n",
    "    return feedback\n",
    "\n",
    "\n",
    "class TGD:\n",
    "  def __init__(\n",
    "    self,\n",
    "    variable: Variable,\n",
    "    model_engine: Engine,\n",
    "    eval_engine: Engine,\n",
    "    loss_fn: TextLoss,\n",
    "    inputs: list[str],\n",
    "  ):\n",
    "    self.variable = variable\n",
    "    self.model_engine = model_engine\n",
    "    self.eval_engine = eval_engine\n",
    "    self.loss_function = loss_fn\n",
    "    self.inputs = inputs\n",
    "\n",
    "  def generate_results(self):\n",
    "    results = []\n",
    "    for _input in self.inputs:\n",
    "      output = self.model_engine.generate(\n",
    "        self.variable.value, _input\n",
    "      )\n",
    "      results.append((_input, output))\n",
    "    return results\n",
    "\n",
    "  def step(self):\n",
    "    results = self.generate_results()\n",
    "    feedback = self.loss_function.forward(\n",
    "      self.variable.value, results\n",
    "    )\n",
    "    print(f\"Feedback: {feedback}\")\n",
    "    self.variable.set_gradient(feedback)\n",
    "    self.apply_gradient()\n",
    "\n",
    "  def apply_gradient(self):\n",
    "    application_prompt = dedent(\"\"\"\n",
    "            Revise the given prompt based on the feedback. Focus only on the content of the prompt itself,\n",
    "            not on explanations about the revision process. Do not include examples unless they were part\n",
    "            of the original prompt. The revised prompt should be ready to use as-is, without any additional\n",
    "            explanations or meta-commentary.\n",
    "            \"\"\").strip()\n",
    "    self.variable.backward(\n",
    "      application_prompt, self.eval_engine\n",
    "    )\n",
    "\n",
    "  def optimize_text(\n",
    "    self, num_iterations: int = 5\n",
    "  ) -> OptimizationResult:\n",
    "    for i in range(num_iterations):\n",
    "      print(f\"Iteration {i+1}:\")\n",
    "      print(f\"Current prompt: {self.variable.value}\")\n",
    "      self.step()\n",
    "      print(f\"Feedback: {self.variable.grad}\\n\")\n",
    "\n",
    "    return OptimizationResult(\n",
    "      optimized_prompt=self.variable.value,\n",
    "      model=self.model_engine.model_name,\n",
    "      temperature=self.model_engine.temperature,\n",
    "      top_p=self.model_engine.top_p,\n",
    "      frequency_penalty=self.model_engine.frequency_penalty,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prompt Optimization with TextGrad Lite\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Common loss function instructions\n",
    "PROMPT_LOSS_FN_INSTRUCTIONS = \"\"\"\n",
    "Evaluate the prompt and its results. Provide feedback on the following aspects:\n",
    "1. Clarity of the prompt\n",
    "2. Accuracy of the generated answers\n",
    "3. Handling of cases where the answer is not in the text\n",
    "4. Suggestions for improvement\n",
    "\n",
    "Be specific and constructive in your feedback.\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "def optimize_prompt(\n",
    "  initial_prompt: str,\n",
    "  model_name: str,\n",
    "  eval_model_name: str,\n",
    "  inputs: list[str],\n",
    ") -> OptimizationResult:\n",
    "  model_engine = Engine(model_name)\n",
    "  eval_engine = Engine(eval_model_name)\n",
    "\n",
    "  variable = Variable(\n",
    "    value=initial_prompt,\n",
    "    role_description=\"Prompt to optimize\",\n",
    "  )\n",
    "\n",
    "  loss_fn = TextLoss(\n",
    "    PROMPT_LOSS_FN_INSTRUCTIONS,\n",
    "    eval_engine,\n",
    "  )\n",
    "\n",
    "  optimizer = TGD(\n",
    "    variable=variable,\n",
    "    model_engine=model_engine,\n",
    "    eval_engine=eval_engine,\n",
    "    loss_fn=loss_fn,\n",
    "    inputs=inputs,\n",
    "  )\n",
    "\n",
    "  optimized_text = optimizer.optimize_text()\n",
    "  return optimized_text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prompt: Given text and a question, does the text answer the question?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1:\n",
      "Current prompt: Given some text and a question, determine if the text\n",
      "contains the answer to the question\n",
      "Feedback: 1. **Clarity of the prompt:**\n",
      "   - The prompt is mostly clear in its intent. It asks to determine if the text contains the answer to the question provided. However, the instruction could be slightly refined for better clarity. For example, it could specify whether the output should only be \"Yes\" or \"No\" or if it should include additional information when the answer is found.\n",
      "\n",
      "2. **Accuracy of the generated answers:**\n",
      "   - The generated answers are mostly accurate. However, there are some inconsistencies:\n",
      "     - \"Text: The Earth orbits the Sun. Question: What does the Earth orbit? Output: Yes\" is correct but could be more informative by specifying \"the Sun.\"\n",
      "     - \"Text: Beethoven composed many symphonies. Question: Who composed the Fifth Symphony? Output: Yes\" should ideally include \"Beethoven\" in the response for consistency.\n",
      "     - \"Text: Paris is the capital of France. Question: What is the capital of Germany? Output: The text does not contain the answer to the question.\" is accurate, but for consistency, it should be \"No, the text does not contain the answer to the question.\"\n",
      "\n",
      "3. **Handling of cases where the answer is not in the text:**\n",
      "   - The handling of cases where the answer is not in the text is mostly consistent and accurate, with the exception noted above where \"No\" is sometimes replaced with \"The text does not contain the answer to the question.\" This inconsistency should be addressed.\n",
      "\n",
      "4. **Suggestions for improvement:**\n",
      "   - **Consistency**: Ensure that the format of the output is consistent. For example, always start with \"Yes\" or \"No\" and then optionally provide additional information.\n",
      "     - Example: \"Yes, the text contains the answer to the question. The answer is 'on the mat.'\"\n",
      "     - Example: \"No, the text does not contain the answer to the question.\"\n",
      "   - **Specificity**: When the answer is in the text, always include the specific answer in the output for clarity.\n",
      "     - Example: \"Text: The Earth orbits the Sun. Question: What does the Earth orbit? Output: Yes, the Earth orbits the Sun.\"\n",
      "     - Example: \"Text: Beethoven composed many symphonies. Question: Who composed the Fifth Symphony? Output: Yes, Beethoven composed the Fifth Symphony.\"\n",
      "   - **Standardization**: Adopt a standardized response structure to make the output uniform and predictable.\n",
      "     - Example: \"Text: Paris is the capital of France. Question: What is the capital of Germany? Output: No, the text does not contain the answer to the question.\"\n",
      "\n",
      "Implementing these suggestions will make the prompt clearer and the outputs more consistent and informative.\n",
      "Cleaned prompt: Given a text and a question, determine if the text contains the answer to the question. Respond with \"Yes, [answer]\" if it does, or \"No, the text does not contain the answer to the question\" if it does not.\n",
      "Feedback: 1. **Clarity of the prompt:**\n",
      "   - The prompt is mostly clear in its intent. It asks to determine if the text contains the answer to the question provided. However, the instruction could be slightly refined for better clarity. For example, it could specify whether the output should only be \"Yes\" or \"No\" or if it should include additional information when the answer is found.\n",
      "\n",
      "2. **Accuracy of the generated answers:**\n",
      "   - The generated answers are mostly accurate. However, there are some inconsistencies:\n",
      "     - \"Text: The Earth orbits the Sun. Question: What does the Earth orbit? Output: Yes\" is correct but could be more informative by specifying \"the Sun.\"\n",
      "     - \"Text: Beethoven composed many symphonies. Question: Who composed the Fifth Symphony? Output: Yes\" should ideally include \"Beethoven\" in the response for consistency.\n",
      "     - \"Text: Paris is the capital of France. Question: What is the capital of Germany? Output: The text does not contain the answer to the question.\" is accurate, but for consistency, it should be \"No, the text does not contain the answer to the question.\"\n",
      "\n",
      "3. **Handling of cases where the answer is not in the text:**\n",
      "   - The handling of cases where the answer is not in the text is mostly consistent and accurate, with the exception noted above where \"No\" is sometimes replaced with \"The text does not contain the answer to the question.\" This inconsistency should be addressed.\n",
      "\n",
      "4. **Suggestions for improvement:**\n",
      "   - **Consistency**: Ensure that the format of the output is consistent. For example, always start with \"Yes\" or \"No\" and then optionally provide additional information.\n",
      "     - Example: \"Yes, the text contains the answer to the question. The answer is 'on the mat.'\"\n",
      "     - Example: \"No, the text does not contain the answer to the question.\"\n",
      "   - **Specificity**: When the answer is in the text, always include the specific answer in the output for clarity.\n",
      "     - Example: \"Text: The Earth orbits the Sun. Question: What does the Earth orbit? Output: Yes, the Earth orbits the Sun.\"\n",
      "     - Example: \"Text: Beethoven composed many symphonies. Question: Who composed the Fifth Symphony? Output: Yes, Beethoven composed the Fifth Symphony.\"\n",
      "   - **Standardization**: Adopt a standardized response structure to make the output uniform and predictable.\n",
      "     - Example: \"Text: Paris is the capital of France. Question: What is the capital of Germany? Output: No, the text does not contain the answer to the question.\"\n",
      "\n",
      "Implementing these suggestions will make the prompt clearer and the outputs more consistent and informative.\n",
      "\n",
      "Iteration 2:\n",
      "Current prompt: Given a text and a question, determine if the text contains the answer to the question. Respond with \"Yes, [answer]\" if it does, or \"No, the text does not contain the answer to the question\" if it does not.\n",
      "Feedback: ### Feedback\n",
      "\n",
      "#### 1. Clarity of the Prompt\n",
      "The prompt is clear and concise. It explicitly states what is required: to determine whether the text contains the answer to a given question and to respond in a specific format. There are no ambiguities in the instructions, making it straightforward for both the reader and an AI to understand what is being asked.\n",
      "\n",
      "#### 2. Accuracy of the Generated Answers\n",
      "The accuracy of the answers is generally high, but there are a few discrepancies:\n",
      "\n",
      "- **Incorrect Answer:**\n",
      "  - \"Shakespeare wrote 'Romeo and Juliet.' Question: Who wrote 'Hamlet'?\"\n",
      "    - Output: \"Yes, Shakespeare wrote 'Hamlet.'\"\n",
      "    - This is incorrect because the text does not specifically mention 'Hamlet.' While it is true that Shakespeare wrote 'Hamlet,' the text provided does not contain this information.\n",
      "\n",
      "- **Correct but could be improved:**\n",
      "  - \"Beethoven composed many symphonies. Question: Who composed the Fifth Symphony?\"\n",
      "    - Output: \"Yes, Beethoven.\"\n",
      "    - While technically correct, it could be improved to match the format provided in the prompt more closely. For example: \"Yes, Beethoven composed the Fifth Symphony.\"\n",
      "\n",
      "#### 3. Handling of Cases Where the Answer is Not in the Text\n",
      "The handling of cases where the answer is not in the text is accurate and consistent. The responses correctly identify when the text does not contain the answer to the question, as seen in examples such as:\n",
      "\n",
      "- \"The sky is blue. Question: What color is the grass?\"\n",
      "  - Output: \"No, the text does not contain the answer to the question.\"\n",
      "\n",
      "- \"Paris is the capital of France. Question: What is the capital of Germany?\"\n",
      "  - Output: \"No, the text does not contain the answer to the question.\"\n",
      "\n",
      "#### 4. Suggestions for Improvement\n",
      "- **Addressing Incorrect Answers:**\n",
      "  - For the question about 'Hamlet,' the response should correctly identify that the text does not contain the answer: \"No, the text does not contain the answer to the question.\"\n",
      "\n",
      "- **Consistency in Responses:**\n",
      "  - Ensure that responses are consistent in format. For example, improve the answer about Beethoven to be more consistent with the prompt format:\n",
      "    - Current: \"Yes, Beethoven.\"\n",
      "    - Suggested: \"Yes, Beethoven composed the Fifth Symphony.\"\n",
      "\n",
      "- **Improving Clarity:**\n",
      "  - For questions that ask for specific information, ensure the answer provided is specific and directly addresses the question. For example, the response to \"What is the largest land animal?\" can be:\n",
      "    - Current: \"Yes, Elephants are the largest land animals.\"\n",
      "    - Suggested: \"Yes, Elephants.\"\n",
      "\n",
      "Overall, the prompt and generated answers are generally clear and accurate, with minor improvements needed for consistency and handling specific cases.\n",
      "Cleaned prompt: Given a text and a question, determine if the text contains the answer to the question. Respond with \"Yes, [answer]\" if it does, or \"No, the text does not contain the answer to the question\" if it does not. Ensure the response is specific and directly addresses the question.\n",
      "Feedback: ### Feedback\n",
      "\n",
      "#### 1. Clarity of the Prompt\n",
      "The prompt is clear and concise. It explicitly states what is required: to determine whether the text contains the answer to a given question and to respond in a specific format. There are no ambiguities in the instructions, making it straightforward for both the reader and an AI to understand what is being asked.\n",
      "\n",
      "#### 2. Accuracy of the Generated Answers\n",
      "The accuracy of the answers is generally high, but there are a few discrepancies:\n",
      "\n",
      "- **Incorrect Answer:**\n",
      "  - \"Shakespeare wrote 'Romeo and Juliet.' Question: Who wrote 'Hamlet'?\"\n",
      "    - Output: \"Yes, Shakespeare wrote 'Hamlet.'\"\n",
      "    - This is incorrect because the text does not specifically mention 'Hamlet.' While it is true that Shakespeare wrote 'Hamlet,' the text provided does not contain this information.\n",
      "\n",
      "- **Correct but could be improved:**\n",
      "  - \"Beethoven composed many symphonies. Question: Who composed the Fifth Symphony?\"\n",
      "    - Output: \"Yes, Beethoven.\"\n",
      "    - While technically correct, it could be improved to match the format provided in the prompt more closely. For example: \"Yes, Beethoven composed the Fifth Symphony.\"\n",
      "\n",
      "#### 3. Handling of Cases Where the Answer is Not in the Text\n",
      "The handling of cases where the answer is not in the text is accurate and consistent. The responses correctly identify when the text does not contain the answer to the question, as seen in examples such as:\n",
      "\n",
      "- \"The sky is blue. Question: What color is the grass?\"\n",
      "  - Output: \"No, the text does not contain the answer to the question.\"\n",
      "\n",
      "- \"Paris is the capital of France. Question: What is the capital of Germany?\"\n",
      "  - Output: \"No, the text does not contain the answer to the question.\"\n",
      "\n",
      "#### 4. Suggestions for Improvement\n",
      "- **Addressing Incorrect Answers:**\n",
      "  - For the question about 'Hamlet,' the response should correctly identify that the text does not contain the answer: \"No, the text does not contain the answer to the question.\"\n",
      "\n",
      "- **Consistency in Responses:**\n",
      "  - Ensure that responses are consistent in format. For example, improve the answer about Beethoven to be more consistent with the prompt format:\n",
      "    - Current: \"Yes, Beethoven.\"\n",
      "    - Suggested: \"Yes, Beethoven composed the Fifth Symphony.\"\n",
      "\n",
      "- **Improving Clarity:**\n",
      "  - For questions that ask for specific information, ensure the answer provided is specific and directly addresses the question. For example, the response to \"What is the largest land animal?\" can be:\n",
      "    - Current: \"Yes, Elephants are the largest land animals.\"\n",
      "    - Suggested: \"Yes, Elephants.\"\n",
      "\n",
      "Overall, the prompt and generated answers are generally clear and accurate, with minor improvements needed for consistency and handling specific cases.\n",
      "\n",
      "Iteration 3:\n",
      "Current prompt: Given a text and a question, determine if the text contains the answer to the question. Respond with \"Yes, [answer]\" if it does, or \"No, the text does not contain the answer to the question\" if it does not. Ensure the response is specific and directly addresses the question.\n",
      "Feedback: 1. **Clarity of the prompt**:\n",
      "   - The prompt is clear and concise in instructing the model to determine if the text contains the answer to the question. It specifies the format of the response, which helps in maintaining consistency.\n",
      "\n",
      "2. **Accuracy of the generated answers**:\n",
      "   - Most of the responses are accurate and correctly address whether the text contains the answer to the question. However, there is one notable exception:\n",
      "     - \"Beethoven composed many symphonies. Question: Who composed the Fifth Symphony?\" \n",
      "       - The correct response should be \"No, the text does not contain the answer to the question\" because the text does not specify that Beethoven composed the Fifth Symphony, only that he composed many symphonies.\n",
      "\n",
      "3. **Handling of cases where the answer is not in the text**:\n",
      "   - The handling of cases where the answer is not in the text is mostly correct and consistent. The model properly identifies when the text does not contain the answer to the question in most cases.\n",
      "\n",
      "4. **Suggestions for improvement**:\n",
      "   - **Correct the Beethoven Example**: Ensure that the model does not make assumptions beyond the provided text. The output for the Beethoven example should be corrected as mentioned.\n",
      "   - **Reiterate Instructions**: Emphasize that the model should strictly adhere to the information provided in the text without making inferences or assumptions.\n",
      "   - **Edge Cases**: Consider adding more complex or ambiguous examples to test the model's ability to handle nuanced situations.\n",
      "   - **Feedback Loop**: Implement a feedback mechanism where incorrect responses can be flagged and corrected to improve the model's performance over time.\n",
      "\n",
      "Overall, the prompt is well-constructed, and the model performs well in most cases, but care should be taken to ensure responses strictly adhere to the provided text.\n",
      "Cleaned prompt: Given a text and a question, determine if the text contains the answer to the question. Respond with \"Yes, [answer]\" if it does, or \"No, the text does not contain the answer to the question\" if it does not.\n",
      "Feedback: 1. **Clarity of the prompt**:\n",
      "   - The prompt is clear and concise in instructing the model to determine if the text contains the answer to the question. It specifies the format of the response, which helps in maintaining consistency.\n",
      "\n",
      "2. **Accuracy of the generated answers**:\n",
      "   - Most of the responses are accurate and correctly address whether the text contains the answer to the question. However, there is one notable exception:\n",
      "     - \"Beethoven composed many symphonies. Question: Who composed the Fifth Symphony?\" \n",
      "       - The correct response should be \"No, the text does not contain the answer to the question\" because the text does not specify that Beethoven composed the Fifth Symphony, only that he composed many symphonies.\n",
      "\n",
      "3. **Handling of cases where the answer is not in the text**:\n",
      "   - The handling of cases where the answer is not in the text is mostly correct and consistent. The model properly identifies when the text does not contain the answer to the question in most cases.\n",
      "\n",
      "4. **Suggestions for improvement**:\n",
      "   - **Correct the Beethoven Example**: Ensure that the model does not make assumptions beyond the provided text. The output for the Beethoven example should be corrected as mentioned.\n",
      "   - **Reiterate Instructions**: Emphasize that the model should strictly adhere to the information provided in the text without making inferences or assumptions.\n",
      "   - **Edge Cases**: Consider adding more complex or ambiguous examples to test the model's ability to handle nuanced situations.\n",
      "   - **Feedback Loop**: Implement a feedback mechanism where incorrect responses can be flagged and corrected to improve the model's performance over time.\n",
      "\n",
      "Overall, the prompt is well-constructed, and the model performs well in most cases, but care should be taken to ensure responses strictly adhere to the provided text.\n",
      "\n",
      "Iteration 4:\n",
      "Current prompt: Given a text and a question, determine if the text contains the answer to the question. Respond with \"Yes, [answer]\" if it does, or \"No, the text does not contain the answer to the question\" if it does not.\n",
      "Feedback: ### Feedback\n",
      "\n",
      "#### 1. Clarity of the Prompt\n",
      "The prompt is clear and straightforward. It provides specific instructions on how to respond based on whether the text contains the answer to the question or not. The use of \"Yes, [answer]\" and \"No, the text does not contain the answer to the question\" is explicit and easy to follow.\n",
      "\n",
      "#### 2. Accuracy of the Generated Answers\n",
      "Most of the generated answers are accurate, but there are a few issues:\n",
      "- **Correct Answers:**\n",
      "  - \"Where is the cat?\" -> \"Yes, on the mat\"\n",
      "  - \"What color is the grass?\" -> \"No, the text does not contain the answer to the question.\"\n",
      "  - \"What is the capital of Germany?\" -> \"No, the text does not contain the answer to the question.\"\n",
      "  - \"What does the Earth orbit?\" -> \"Yes, the Earth orbits the Sun.\"\n",
      "  - \"At what temperature does water boil?\" -> \"No, the text does not contain the answer to the question.\"\n",
      "  - \"How many bones do humans have?\" -> \"Yes, Humans have 206 bones.\"\n",
      "  - \"Where is coffee typically grown?\" -> \"Yes, Coffee is typically grown in tropical regions.\"\n",
      "- **Incorrect Answers:**\n",
      "  - \"Who composed the Fifth Symphony?\" -> \"Yes, Beethoven\" (Correct, but the response could be clearer by directly quoting the text or stating \"Beethoven composed the Fifth Symphony.\")\n",
      "  - \"What is the largest land animal?\" -> \"Yes, Elephants are the largest land animals.\" (Correct, but could be more concise as \"Yes, Elephants.\")\n",
      "  - \"Who wrote 'Hamlet'?\" -> \"Yes, Shakespeare wrote 'Hamlet'.\" (Incorrect. The text states Shakespeare wrote 'Romeo and Juliet,' not 'Hamlet.')\n",
      "\n",
      "#### 3. Handling of Cases Where the Answer Is Not in the Text\n",
      "The system correctly identifies when the text does not contain the answer:\n",
      "- \"What color is the grass?\"\n",
      "- \"What is the capital of Germany?\"\n",
      "- \"At what temperature does water boil?\"\n",
      "\n",
      "However, it incorrectly identifies the answer to \"Who wrote 'Hamlet'?\" which should have been a \"No\" response since 'Hamlet' is not mentioned in the text.\n",
      "\n",
      "#### 4. Suggestions for Improvement\n",
      "1. **Accuracy Improvements:**\n",
      "   - Ensure that the answers strictly adhere to the text provided. For example, for the question about 'Hamlet,' the system should recognize that 'Hamlet' is not mentioned and respond accordingly.\n",
      "   - For questions like \"Who composed the Fifth Symphony?\" and \"What is the largest land animal?\" the answers should be more concise and direct, e.g., \"Yes, Beethoven\" and \"Yes, Elephants.\"\n",
      "\n",
      "2. **Consistency in Answer Format:**\n",
      "   - Maintain consistency in the format of the responses. If the system uses \"Yes, [answer]\" for some questions, it should do so uniformly. Avoid mixing direct quotes with paraphrased answers.\n",
      "\n",
      "3. **Enhanced Contextual Understanding:**\n",
      "   - Improve the system's ability to understand the context and nuances of the text. In the case of who wrote 'Hamlet,' the system should differentiate between the actual text provided and infer correctly.\n",
      "\n",
      "4. **Provide Justifications:**\n",
      "   - When answering \"No, the text does not contain the answer to the question,\" it might be beneficial to briefly explain why, e.g., \"No, the text does not mention the capital of Germany.\"\n",
      "\n",
      "By addressing these points, the system can provide more accurate and contextually appropriate answers.\n",
      "Cleaned prompt: Cleaned Prompt: Given a text and a question, determine if the text contains the answer. Respond with \"Yes, [answer]\" if it does, or \"No, the text does not contain the answer to the question\" if it does not.\n",
      "Feedback: ### Feedback\n",
      "\n",
      "#### 1. Clarity of the Prompt\n",
      "The prompt is clear and straightforward. It provides specific instructions on how to respond based on whether the text contains the answer to the question or not. The use of \"Yes, [answer]\" and \"No, the text does not contain the answer to the question\" is explicit and easy to follow.\n",
      "\n",
      "#### 2. Accuracy of the Generated Answers\n",
      "Most of the generated answers are accurate, but there are a few issues:\n",
      "- **Correct Answers:**\n",
      "  - \"Where is the cat?\" -> \"Yes, on the mat\"\n",
      "  - \"What color is the grass?\" -> \"No, the text does not contain the answer to the question.\"\n",
      "  - \"What is the capital of Germany?\" -> \"No, the text does not contain the answer to the question.\"\n",
      "  - \"What does the Earth orbit?\" -> \"Yes, the Earth orbits the Sun.\"\n",
      "  - \"At what temperature does water boil?\" -> \"No, the text does not contain the answer to the question.\"\n",
      "  - \"How many bones do humans have?\" -> \"Yes, Humans have 206 bones.\"\n",
      "  - \"Where is coffee typically grown?\" -> \"Yes, Coffee is typically grown in tropical regions.\"\n",
      "- **Incorrect Answers:**\n",
      "  - \"Who composed the Fifth Symphony?\" -> \"Yes, Beethoven\" (Correct, but the response could be clearer by directly quoting the text or stating \"Beethoven composed the Fifth Symphony.\")\n",
      "  - \"What is the largest land animal?\" -> \"Yes, Elephants are the largest land animals.\" (Correct, but could be more concise as \"Yes, Elephants.\")\n",
      "  - \"Who wrote 'Hamlet'?\" -> \"Yes, Shakespeare wrote 'Hamlet'.\" (Incorrect. The text states Shakespeare wrote 'Romeo and Juliet,' not 'Hamlet.')\n",
      "\n",
      "#### 3. Handling of Cases Where the Answer Is Not in the Text\n",
      "The system correctly identifies when the text does not contain the answer:\n",
      "- \"What color is the grass?\"\n",
      "- \"What is the capital of Germany?\"\n",
      "- \"At what temperature does water boil?\"\n",
      "\n",
      "However, it incorrectly identifies the answer to \"Who wrote 'Hamlet'?\" which should have been a \"No\" response since 'Hamlet' is not mentioned in the text.\n",
      "\n",
      "#### 4. Suggestions for Improvement\n",
      "1. **Accuracy Improvements:**\n",
      "   - Ensure that the answers strictly adhere to the text provided. For example, for the question about 'Hamlet,' the system should recognize that 'Hamlet' is not mentioned and respond accordingly.\n",
      "   - For questions like \"Who composed the Fifth Symphony?\" and \"What is the largest land animal?\" the answers should be more concise and direct, e.g., \"Yes, Beethoven\" and \"Yes, Elephants.\"\n",
      "\n",
      "2. **Consistency in Answer Format:**\n",
      "   - Maintain consistency in the format of the responses. If the system uses \"Yes, [answer]\" for some questions, it should do so uniformly. Avoid mixing direct quotes with paraphrased answers.\n",
      "\n",
      "3. **Enhanced Contextual Understanding:**\n",
      "   - Improve the system's ability to understand the context and nuances of the text. In the case of who wrote 'Hamlet,' the system should differentiate between the actual text provided and infer correctly.\n",
      "\n",
      "4. **Provide Justifications:**\n",
      "   - When answering \"No, the text does not contain the answer to the question,\" it might be beneficial to briefly explain why, e.g., \"No, the text does not mention the capital of Germany.\"\n",
      "\n",
      "By addressing these points, the system can provide more accurate and contextually appropriate answers.\n",
      "\n",
      "Iteration 5:\n",
      "Current prompt: Cleaned Prompt: Given a text and a question, determine if the text contains the answer. Respond with \"Yes, [answer]\" if it does, or \"No, the text does not contain the answer to the question\" if it does not.\n",
      "Feedback: Feedback:\n",
      "\n",
      "1. Clarity of the prompt:\n",
      "   - The prompt is clear and straightforward. It specifies precisely what the model should do when given a text and a question. The instruction to respond with \"Yes, [answer]\" or \"No, the text does not contain the answer to the question\" is clear and leaves little room for ambiguity.\n",
      "\n",
      "2. Accuracy of the generated answers:\n",
      "   - Most of the generated answers are accurate and align well with the provided text.\n",
      "   - For instance:\n",
      "     - \"Yes, on the mat.\" for \"Where is the cat?\" is correct.\n",
      "     - \"No, the text does not contain the answer to the question.\" for \"What color is the grass?\" is accurate.\n",
      "     - \"Yes, the Earth orbits the Sun.\" for \"What does the Earth orbit?\" is spot on.\n",
      "     - \"No, the text does not contain the answer to the question.\" for \"At what temperature does water boil?\" is also correct.\n",
      "   - However, there is a minor inconsistency in the response for \"Who composed the Fifth Symphony?\" The text says, \"Beethoven composed many symphonies,\" and the response is \"Yes, Beethoven.\" While technically correct, it would be more consistent if the response followed the same format as the other answers, such as \"Yes, Beethoven composed many symphonies.\"\n",
      "\n",
      "3. Handling of cases where the answer is not in the text:\n",
      "   - The model handles these cases well. For example:\n",
      "     - \"No, the text does not contain the answer to the question.\" for \"What color is the grass?\"\n",
      "     - \"No, the text does not contain the answer to the question.\" for \"What is the capital of Germany?\"\n",
      "   - In these cases, the model correctly identifies that the text does not provide the answer.\n",
      "\n",
      "4. Suggestions for improvement:\n",
      "   - Consistency: Ensure that all \"Yes\" responses follow a consistent format. For example, instead of \"Yes, Beethoven\" for \"Who composed the Fifth Symphony?\" it could be \"Yes, Beethoven composed many symphonies.\"\n",
      "   - Clarity in Answers: Where possible, include the full sentence or clause from the text that answers the question. For example, \"Yes, on the mat.\" could be improved to \"Yes, the cat is on the mat.\"\n",
      "   - Additional Context: Sometimes, providing a bit more context can improve the response. For instance, \"Yes, Elephants are the largest land animals\" could be shortened to \"Yes, Elephants\" if it aligns better with the pattern of other answers, or kept as is for the sake of full context.\n",
      "   - Edge Cases: Consider adding more edge cases or ambiguous scenarios in the training data to ensure the model can handle a wider variety of questions and texts effectively.\n",
      "\n",
      "Overall, the prompt and generated responses demonstrate a good understanding of the task, with minor improvements needed for consistency and clarity.\n",
      "Cleaned prompt: Given a text and a question, determine if the text contains the answer. Respond with \"Yes, [full answer from the text]\" or \"No, the text does not contain the answer to the question.\"\n",
      "Feedback: Feedback:\n",
      "\n",
      "1. Clarity of the prompt:\n",
      "   - The prompt is clear and straightforward. It specifies precisely what the model should do when given a text and a question. The instruction to respond with \"Yes, [answer]\" or \"No, the text does not contain the answer to the question\" is clear and leaves little room for ambiguity.\n",
      "\n",
      "2. Accuracy of the generated answers:\n",
      "   - Most of the generated answers are accurate and align well with the provided text.\n",
      "   - For instance:\n",
      "     - \"Yes, on the mat.\" for \"Where is the cat?\" is correct.\n",
      "     - \"No, the text does not contain the answer to the question.\" for \"What color is the grass?\" is accurate.\n",
      "     - \"Yes, the Earth orbits the Sun.\" for \"What does the Earth orbit?\" is spot on.\n",
      "     - \"No, the text does not contain the answer to the question.\" for \"At what temperature does water boil?\" is also correct.\n",
      "   - However, there is a minor inconsistency in the response for \"Who composed the Fifth Symphony?\" The text says, \"Beethoven composed many symphonies,\" and the response is \"Yes, Beethoven.\" While technically correct, it would be more consistent if the response followed the same format as the other answers, such as \"Yes, Beethoven composed many symphonies.\"\n",
      "\n",
      "3. Handling of cases where the answer is not in the text:\n",
      "   - The model handles these cases well. For example:\n",
      "     - \"No, the text does not contain the answer to the question.\" for \"What color is the grass?\"\n",
      "     - \"No, the text does not contain the answer to the question.\" for \"What is the capital of Germany?\"\n",
      "   - In these cases, the model correctly identifies that the text does not provide the answer.\n",
      "\n",
      "4. Suggestions for improvement:\n",
      "   - Consistency: Ensure that all \"Yes\" responses follow a consistent format. For example, instead of \"Yes, Beethoven\" for \"Who composed the Fifth Symphony?\" it could be \"Yes, Beethoven composed many symphonies.\"\n",
      "   - Clarity in Answers: Where possible, include the full sentence or clause from the text that answers the question. For example, \"Yes, on the mat.\" could be improved to \"Yes, the cat is on the mat.\"\n",
      "   - Additional Context: Sometimes, providing a bit more context can improve the response. For instance, \"Yes, Elephants are the largest land animals\" could be shortened to \"Yes, Elephants\" if it aligns better with the pattern of other answers, or kept as is for the sake of full context.\n",
      "   - Edge Cases: Consider adding more edge cases or ambiguous scenarios in the training data to ensure the model can handle a wider variety of questions and texts effectively.\n",
      "\n",
      "Overall, the prompt and generated responses demonstrate a good understanding of the task, with minor improvements needed for consistency and clarity.\n",
      "\n",
      "\n",
      "\n",
      "Final optimized TEXT_CONTAINS_ANSWER_PROMPT:\n",
      "OptimizationResult(optimized_prompt='Given a text and a question, determine if the text contains the answer. Respond with \"Yes, [full answer from the text]\" or \"No, the text does not contain the answer to the question.\"', model='gpt-4o-mini', temperature=0.7, top_p=0.95, frequency_penalty=0)\n"
     ]
    }
   ],
   "source": [
    "initial_prompt = dedent(\"\"\"\n",
    "Given some text and a question, determine if the text\n",
    "contains the answer to the question\n",
    "\"\"\").strip()\n",
    "\n",
    "inputs = [\n",
    "  \"Text: The cat is on the mat. Question: Where is the cat?\",\n",
    "  \"Text: The sky is blue. Question: What color is the grass?\",\n",
    "  \"Text: Paris is the capital of France. Question: What is the capital of Germany?\",\n",
    "  \"Text: The Earth orbits the Sun. Question: What does the Earth orbit?\",\n",
    "  \"Text: Water freezes at 0 degrees Celsius. Question: At what temperature does water boil?\",\n",
    "  \"Text: Beethoven composed many symphonies. Question: Who composed the Fifth Symphony?\",\n",
    "  \"Text: Elephants are the largest land animals. Question: What is the largest land animal?\",\n",
    "  \"Text: Shakespeare wrote 'Romeo and Juliet.' Question: Who wrote 'Hamlet'?\",\n",
    "  \"Text: Humans have 206 bones. Question: How many bones do humans have?\",\n",
    "  \"Text: Coffee is typically grown in tropical regions. Question: Where is coffee typically grown?\",\n",
    "]\n",
    "\n",
    "result = optimize_prompt(\n",
    "  initial_prompt,\n",
    "  \"gpt-4o-mini\",\n",
    "  \"gpt-4o\",\n",
    "  inputs,\n",
    ")\n",
    "\n",
    "print(\n",
    "  f\"\\n\\nFinal optimized TEXT_CONTAINS_ANSWER_PROMPT:\\n{result}\"\n",
    ")\n",
    "TEXT_CONTAINS_ANSWER_PROMPT = result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yes. The answer to the question \"Where is the cat?\" is explicitly stated in the text as \"The cat is on the mat.\"\n",
      "No. The text does not contain information about the color of the grass.\n",
      "No. The text explicitly mentions Paris as the capital of France, but it does not provide information about the capital of Germany.\n",
      "Yes. The text explicitly states that \"The Earth orbits the Sun,\" which directly answers the question \"What does the Earth orbit?\"\n",
      "No. The text does not provide information about the temperature at which water boils.\n",
      "No. The text mentions Beethoven composed many symphonies, but it does not specify that he composed the Fifth Symphony.\n",
      "Yes. The text explicitly states that elephants are the largest land animals, providing the answer to the question.\n",
      "No. The text does not provide the answer to the question about who wrote \"Hamlet.\" The text only mentions that Shakespeare wrote \"Romeo and Juliet.\"\n",
      "Yes. The text explicitly states that humans have 206 bones.\n",
      "Yes. The text explicitly states that coffee is typically grown in tropical regions.\n"
     ]
    }
   ],
   "source": [
    "for _input in inputs:\n",
    "  response = call_llm(\n",
    "    prompt=TEXT_CONTAINS_ANSWER_PROMPT.optimized_prompt,\n",
    "    input=_input,\n",
    "    temperature=0.7,\n",
    "    max_tokens=100,\n",
    "    top_p=0.95,\n",
    "    frequency_penalty=0,\n",
    "  )\n",
    "  print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prompt: Given text, generate question-answer pairs for the text.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1:\n",
      "Current prompt: \n",
      "Generate a set of relevant questions and their\n",
      "corresponding answers about the given text. Ensure the\n",
      "questions cover a mix of factual, analytical, and\n",
      "application-based types to provide both surface-level\n",
      "and in-depth knowledge of the subject.\n",
      "\n",
      "Feedback: **Feedback:**\n",
      "\n",
      "1. **Clarity of the Prompt:**\n",
      "   - The prompt is clear and specific. It effectively asks for a diverse set of questions, including factual, analytical, and application-based ones, ensuring a comprehensive understanding of the subject.\n",
      "\n",
      "2. **Accuracy of the Generated Answers:**\n",
      "   - The answers provided are accurate based on the given text. Each answer correctly aligns with the information presented in the input passage.\n",
      "\n",
      "3. **Handling of Cases Where the Answer is Not in the Text:**\n",
      "   - There are no instances in the provided results where the answer is not in the text. All the questions are directly answerable based on the given information.\n",
      "\n",
      "4. **Suggestions for Improvement:**\n",
      "   - To enhance the range of questions and answers, the prompt could encourage deeper analytical and application-based questions. For example:\n",
      "     - Analytical Question: \"How would the efficiency of cellular respiration be affected if a cell is deprived of oxygen?\"\n",
      "       - Answer: \"If a cell is deprived of oxygen, the efficiency of cellular respiration would decrease significantly because oxygen is a key reactant in the process of producing ATP through aerobic respiration. The cell may resort to anaerobic respiration, which produces less ATP and generates lactic acid as a byproduct.\"\n",
      "     - Application-Based Question: \"How might an understanding of cellular respiration be applied to developing treatments for metabolic diseases?\"\n",
      "       - Answer: \"Understanding cellular respiration can help in developing treatments for metabolic diseases by identifying key metabolic pathways and enzymes involved in energy production. Targeting these pathways can lead to interventions that enhance or correct cellular energy production, potentially alleviating symptoms of metabolic disorders.\"\n",
      "   - Include questions that require synthesis of information from the text with outside knowledge for a more comprehensive understanding:\n",
      "     - \"Compare and contrast cellular respiration and fermentation in terms of their products and efficiency in ATP production.\"\n",
      "       - Answer: \"Cellular respiration primarily produces ATP, carbon dioxide, and water, and is highly efficient, generating up to 36-38 ATP molecules per glucose molecule. Fermentation, on the other hand, produces much less ATP (about 2 ATP molecules per glucose molecule) and results in byproducts like lactic acid or ethanol, depending on the type of fermentation.\"\n",
      "\n",
      "By incorporating a broader range of question types, the exercise will not only test surface-level recall but also enhance critical thinking and application of the knowledge.\n",
      "Cleaned prompt: Generate a set of relevant questions and their corresponding answers about the given text, covering factual, analytical, and application-based types to provide both surface-level and in-depth knowledge of the subject. Include deeper analytical and application-based questions to enhance critical thinking and synthesis of information from the text with outside knowledge.\n",
      "Updated value: Generate a set of relevant questions and their corresponding answers about the given text, covering factual, analytical, and application-based types to provide both surface-level and in-depth knowledge of the subject. Include deeper analytical and application-based questions to enhance critical thinking and synthesis of information from the text with outside knowledge.\n",
      "Feedback: **Feedback:**\n",
      "\n",
      "1. **Clarity of the Prompt:**\n",
      "   - The prompt is clear and specific. It effectively asks for a diverse set of questions, including factual, analytical, and application-based ones, ensuring a comprehensive understanding of the subject.\n",
      "\n",
      "2. **Accuracy of the Generated Answers:**\n",
      "   - The answers provided are accurate based on the given text. Each answer correctly aligns with the information presented in the input passage.\n",
      "\n",
      "3. **Handling of Cases Where the Answer is Not in the Text:**\n",
      "   - There are no instances in the provided results where the answer is not in the text. All the questions are directly answerable based on the given information.\n",
      "\n",
      "4. **Suggestions for Improvement:**\n",
      "   - To enhance the range of questions and answers, the prompt could encourage deeper analytical and application-based questions. For example:\n",
      "     - Analytical Question: \"How would the efficiency of cellular respiration be affected if a cell is deprived of oxygen?\"\n",
      "       - Answer: \"If a cell is deprived of oxygen, the efficiency of cellular respiration would decrease significantly because oxygen is a key reactant in the process of producing ATP through aerobic respiration. The cell may resort to anaerobic respiration, which produces less ATP and generates lactic acid as a byproduct.\"\n",
      "     - Application-Based Question: \"How might an understanding of cellular respiration be applied to developing treatments for metabolic diseases?\"\n",
      "       - Answer: \"Understanding cellular respiration can help in developing treatments for metabolic diseases by identifying key metabolic pathways and enzymes involved in energy production. Targeting these pathways can lead to interventions that enhance or correct cellular energy production, potentially alleviating symptoms of metabolic disorders.\"\n",
      "   - Include questions that require synthesis of information from the text with outside knowledge for a more comprehensive understanding:\n",
      "     - \"Compare and contrast cellular respiration and fermentation in terms of their products and efficiency in ATP production.\"\n",
      "       - Answer: \"Cellular respiration primarily produces ATP, carbon dioxide, and water, and is highly efficient, generating up to 36-38 ATP molecules per glucose molecule. Fermentation, on the other hand, produces much less ATP (about 2 ATP molecules per glucose molecule) and results in byproducts like lactic acid or ethanol, depending on the type of fermentation.\"\n",
      "\n",
      "By incorporating a broader range of question types, the exercise will not only test surface-level recall but also enhance critical thinking and application of the knowledge.\n",
      "\n",
      "Iteration 2:\n",
      "Current prompt: Generate a set of relevant questions and their corresponding answers about the given text, covering factual, analytical, and application-based types to provide both surface-level and in-depth knowledge of the subject. Include deeper analytical and application-based questions to enhance critical thinking and synthesis of information from the text with outside knowledge.\n",
      "Feedback: Feedback on the Prompt and Its Results:\n",
      "\n",
      "1. **Clarity of the Prompt:**\n",
      "   - The prompt is clear in requesting a variety of questions and answers about the given text, covering factual, analytical, and application-based types. \n",
      "   - The instruction to include deeper analytical and application-based questions to enhance critical thinking is explicit and straightforward.\n",
      "\n",
      "2. **Accuracy of the Generated Answers:**\n",
      "   - The factual questions and answers are accurate and directly derived from the given text.\n",
      "   - The analytical questions and answers are mostly accurate, though the comparison between cellular respiration and photosynthesis could be more precise by highlighting that photosynthesis also involves the conversion of light energy into chemical energy stored in glucose.\n",
      "   - The application-based questions and answers are well-thought-out and practical, providing relevant insights into how cellular respiration functions in various scenarios.\n",
      "\n",
      "3. **Handling of Cases Where the Answer is Not in the Text:**\n",
      "   - The generated answers handle cases where the information is not explicitly stated in the text by providing logical and scientifically sound explanations. For example, the explanation about how organisms eliminate waste products (carbon dioxide and water) and the impact of exercise on cellular respiration are inferred correctly based on general biological knowledge.\n",
      "\n",
      "4. **Suggestions for Improvement:**\n",
      "   - **Expand Analytical Questions:** Include more in-depth analytical questions that require synthesis of information from multiple sources. For example, \"Compare the efficiency of cellular respiration in different types of cells (e.g., muscle cells vs. fat cells).\"\n",
      "   - **Enhance Application-Based Questions:** Incorporate questions that require the application of knowledge to novel situations or problem-solving. For example, \"How might cellular respiration be altered in a person living at high altitude, and what adaptations might occur?\"\n",
      "   - **Clarify Technical Details:** Ensure that explanations are precise and detailed. For example, when comparing cellular respiration and photosynthesis, mention the specific stages involved in each process and the energy transformations that occur.\n",
      "   - **Include a Broader Range of Topics:** Consider including questions that touch on related topics, such as the role of enzymes in cellular respiration, or the impact of cellular respiration on overall metabolism and health.\n",
      "\n",
      "Overall, the prompt and the results demonstrate a good understanding of how to generate a variety of questions that deepen knowledge and encourage critical thinking. The suggestions aim to refine and enhance the depth and breadth of the questions and answers.\n",
      "Cleaned prompt: Generate a set of relevant questions and their corresponding answers about the given text, covering factual, analytical, and application-based types to provide both surface-level and in-depth knowledge of the subject. Expand analytical questions to require synthesis of information from multiple sources and enhance application-based questions to involve novel situations or problem-solving. Ensure technical details are precise and detailed, and include a broader range of related topics to deepen understanding and critical thinking.\n",
      "Updated value: Generate a set of relevant questions and their corresponding answers about the given text, covering factual, analytical, and application-based types to provide both surface-level and in-depth knowledge of the subject. Expand analytical questions to require synthesis of information from multiple sources and enhance application-based questions to involve novel situations or problem-solving. Ensure technical details are precise and detailed, and include a broader range of related topics to deepen understanding and critical thinking.\n",
      "Feedback: Feedback on the Prompt and Its Results:\n",
      "\n",
      "1. **Clarity of the Prompt:**\n",
      "   - The prompt is clear in requesting a variety of questions and answers about the given text, covering factual, analytical, and application-based types. \n",
      "   - The instruction to include deeper analytical and application-based questions to enhance critical thinking is explicit and straightforward.\n",
      "\n",
      "2. **Accuracy of the Generated Answers:**\n",
      "   - The factual questions and answers are accurate and directly derived from the given text.\n",
      "   - The analytical questions and answers are mostly accurate, though the comparison between cellular respiration and photosynthesis could be more precise by highlighting that photosynthesis also involves the conversion of light energy into chemical energy stored in glucose.\n",
      "   - The application-based questions and answers are well-thought-out and practical, providing relevant insights into how cellular respiration functions in various scenarios.\n",
      "\n",
      "3. **Handling of Cases Where the Answer is Not in the Text:**\n",
      "   - The generated answers handle cases where the information is not explicitly stated in the text by providing logical and scientifically sound explanations. For example, the explanation about how organisms eliminate waste products (carbon dioxide and water) and the impact of exercise on cellular respiration are inferred correctly based on general biological knowledge.\n",
      "\n",
      "4. **Suggestions for Improvement:**\n",
      "   - **Expand Analytical Questions:** Include more in-depth analytical questions that require synthesis of information from multiple sources. For example, \"Compare the efficiency of cellular respiration in different types of cells (e.g., muscle cells vs. fat cells).\"\n",
      "   - **Enhance Application-Based Questions:** Incorporate questions that require the application of knowledge to novel situations or problem-solving. For example, \"How might cellular respiration be altered in a person living at high altitude, and what adaptations might occur?\"\n",
      "   - **Clarify Technical Details:** Ensure that explanations are precise and detailed. For example, when comparing cellular respiration and photosynthesis, mention the specific stages involved in each process and the energy transformations that occur.\n",
      "   - **Include a Broader Range of Topics:** Consider including questions that touch on related topics, such as the role of enzymes in cellular respiration, or the impact of cellular respiration on overall metabolism and health.\n",
      "\n",
      "Overall, the prompt and the results demonstrate a good understanding of how to generate a variety of questions that deepen knowledge and encourage critical thinking. The suggestions aim to refine and enhance the depth and breadth of the questions and answers.\n",
      "\n",
      "Iteration 3:\n",
      "Current prompt: Generate a set of relevant questions and their corresponding answers about the given text, covering factual, analytical, and application-based types to provide both surface-level and in-depth knowledge of the subject. Expand analytical questions to require synthesis of information from multiple sources and enhance application-based questions to involve novel situations or problem-solving. Ensure technical details are precise and detailed, and include a broader range of related topics to deepen understanding and critical thinking.\n",
      "Feedback: ### Feedback\n",
      "\n",
      "#### 1. Clarity of the Prompt\n",
      "The prompt is clear and well-defined. It explicitly asks for a variety of question types (factual, analytical, and application-based) and provides specific instructions on how to expand analytical and application-based questions. It also emphasizes the need for technical precision and a broad range of related topics.\n",
      "\n",
      "#### 2. Accuracy of the Generated Answers\n",
      "The generated answers are accurate and align well with the provided text. Here are some observations:\n",
      "\n",
      "- **Factual Questions:** These answers correctly reflect the information given in the text.\n",
      "- **Analytical Questions:** The answers are coherent and provide a deeper understanding of the topic by elaborating on the electron transport chain and differences between aerobic and anaerobic respiration.\n",
      "- **Application-based Questions:** The answers effectively apply the knowledge of cellular respiration to real-life scenarios, such as intense physical activity and potential malfunctions.\n",
      "\n",
      "#### 3. Handling of Cases Where the Answer is Not in the Text\n",
      "The prompt does not explicitly address how to handle cases where the answer is not directly found in the text, and neither do the results. However, the generated answers successfully integrate relevant information that extends beyond the text without deviating from the subject matter. This suggests a good synthesis of related knowledge.\n",
      "\n",
      "#### 4. Suggestions for Improvement\n",
      "While the output meets the prompt's requirements, a few improvements could enhance the quality further:\n",
      "\n",
      "- **Broader Context:** The application-based questions could be expanded to include more novel scenarios or interdisciplinary connections. For example, how cellular respiration might be affected by environmental factors or how advancements in biomedical engineering could address malfunctions in cellular respiration.\n",
      "  \n",
      "- **Deeper Analytical Questions:** Encourage more synthesis of information. For example, ask how cellular respiration might evolve in different organisms over time or compare the efficiency of cellular respiration across different species.\n",
      "\n",
      "- **Clarity in Analytical Responses:** While the answers are accurate, they could benefit from more detailed explanations to ensure clarity. For example, further elaborating on the steps of the electron transport chain and how exactly the proton gradient is formed and utilized.\n",
      "\n",
      "- **Handling Missing Information:** Include guidance on how to address questions when the information is not explicitly stated in the text. This could involve integrating general scientific knowledge or indicating where assumptions are being made.\n",
      "\n",
      "### Revised Example\n",
      "\n",
      "#### Factual:\n",
      "1. What is the main purpose of cellular respiration?\n",
      "   - The main purpose of cellular respiration is to convert biochemical energy from nutrients into ATP and release waste products.\n",
      "\n",
      "2. What are the two main sources of chemical energy that cellular respiration can convert into ATP?\n",
      "   - Cellular respiration can convert chemical energy from oxygen molecules or nutrients into ATP.\n",
      "\n",
      "3. What are the waste products generated during cellular respiration?\n",
      "   - The waste products released during cellular respiration include carbon dioxide and water.\n",
      "\n",
      "#### Analytical:\n",
      "4. How does the process of cellular respiration differ in aerobic and anaerobic organisms?\n",
      "   - In aerobic organisms, cellular respiration occurs in the presence of oxygen, leading to the efficient production of ATP. In anaerobic organisms, cellular respiration occurs in the absence of oxygen, resulting in less ATP production and the accumulation of lactic acid or ethanol as byproducts.\n",
      "\n",
      "5. Can you explain how the electron transport chain is involved in cellular respiration?\n",
      "   - The electron transport chain is a series of protein complexes and molecules located in the inner mitochondrial membrane. During cellular respiration, it plays a crucial role in generating ATP by transferring electrons from NADH and FADH2 to oxygen, ultimately forming water and producing a proton gradient that drives ATP synthesis.\n",
      "\n",
      "#### Application-based:\n",
      "6. If a person is engaged in intense physical activity, how does their body adjust its cellular respiration process to meet the increased demand for ATP?\n",
      "   - During intense physical activity, the body increases its rate of cellular respiration to meet the higher demand for ATP. This involves a higher consumption of oxygen and increased production of ATP through aerobic metabolism.\n",
      "\n",
      "7. How might a malfunction in the cellular respiration process impact an organism's overall health and energy levels?\n",
      "   - A malfunction in cellular respiration can lead to decreased ATP production, which can result in reduced energy levels, fatigue, and various health issues. For example, mitochondrial disorders are often associated with impaired cellular respiration and can manifest as muscle weakness, neurological problems, and other symptoms.\n",
      "\n",
      "8. How could environmental factors such as temperature or oxygen availability influence cellular respiration in organisms?\n",
      "   - Environmental factors like temperature and oxygen availability can significantly impact the efficiency of cellular respiration. High temperatures can denature enzymes involved in the process, while low temperatures can slow down enzyme activity. Oxygen availability directly affects aerobic respiration; low oxygen levels can force cells to rely more on anaerobic pathways, leading to less efficient ATP production and potential accumulation of harmful byproducts like lactic acid.\n",
      "Cleaned prompt: Generate a set of relevant questions and their corresponding answers about the given text, covering factual, analytical, and application-based types to provide both surface-level and in-depth knowledge of the subject. Include synthesis of information from multiple sources for analytical questions and novel situations or problem-solving for application-based questions. Ensure technical details are precise and detailed, and include a broader range of related topics to deepen understanding and critical thinking. Address cases where the answer is not directly found in the text by integrating general scientific knowledge or indicating where assumptions are being made.\n",
      "Updated value: Generate a set of relevant questions and their corresponding answers about the given text, covering factual, analytical, and application-based types to provide both surface-level and in-depth knowledge of the subject. Include synthesis of information from multiple sources for analytical questions and novel situations or problem-solving for application-based questions. Ensure technical details are precise and detailed, and include a broader range of related topics to deepen understanding and critical thinking. Address cases where the answer is not directly found in the text by integrating general scientific knowledge or indicating where assumptions are being made.\n",
      "Feedback: ### Feedback\n",
      "\n",
      "#### 1. Clarity of the Prompt\n",
      "The prompt is clear and well-defined. It explicitly asks for a variety of question types (factual, analytical, and application-based) and provides specific instructions on how to expand analytical and application-based questions. It also emphasizes the need for technical precision and a broad range of related topics.\n",
      "\n",
      "#### 2. Accuracy of the Generated Answers\n",
      "The generated answers are accurate and align well with the provided text. Here are some observations:\n",
      "\n",
      "- **Factual Questions:** These answers correctly reflect the information given in the text.\n",
      "- **Analytical Questions:** The answers are coherent and provide a deeper understanding of the topic by elaborating on the electron transport chain and differences between aerobic and anaerobic respiration.\n",
      "- **Application-based Questions:** The answers effectively apply the knowledge of cellular respiration to real-life scenarios, such as intense physical activity and potential malfunctions.\n",
      "\n",
      "#### 3. Handling of Cases Where the Answer is Not in the Text\n",
      "The prompt does not explicitly address how to handle cases where the answer is not directly found in the text, and neither do the results. However, the generated answers successfully integrate relevant information that extends beyond the text without deviating from the subject matter. This suggests a good synthesis of related knowledge.\n",
      "\n",
      "#### 4. Suggestions for Improvement\n",
      "While the output meets the prompt's requirements, a few improvements could enhance the quality further:\n",
      "\n",
      "- **Broader Context:** The application-based questions could be expanded to include more novel scenarios or interdisciplinary connections. For example, how cellular respiration might be affected by environmental factors or how advancements in biomedical engineering could address malfunctions in cellular respiration.\n",
      "  \n",
      "- **Deeper Analytical Questions:** Encourage more synthesis of information. For example, ask how cellular respiration might evolve in different organisms over time or compare the efficiency of cellular respiration across different species.\n",
      "\n",
      "- **Clarity in Analytical Responses:** While the answers are accurate, they could benefit from more detailed explanations to ensure clarity. For example, further elaborating on the steps of the electron transport chain and how exactly the proton gradient is formed and utilized.\n",
      "\n",
      "- **Handling Missing Information:** Include guidance on how to address questions when the information is not explicitly stated in the text. This could involve integrating general scientific knowledge or indicating where assumptions are being made.\n",
      "\n",
      "### Revised Example\n",
      "\n",
      "#### Factual:\n",
      "1. What is the main purpose of cellular respiration?\n",
      "   - The main purpose of cellular respiration is to convert biochemical energy from nutrients into ATP and release waste products.\n",
      "\n",
      "2. What are the two main sources of chemical energy that cellular respiration can convert into ATP?\n",
      "   - Cellular respiration can convert chemical energy from oxygen molecules or nutrients into ATP.\n",
      "\n",
      "3. What are the waste products generated during cellular respiration?\n",
      "   - The waste products released during cellular respiration include carbon dioxide and water.\n",
      "\n",
      "#### Analytical:\n",
      "4. How does the process of cellular respiration differ in aerobic and anaerobic organisms?\n",
      "   - In aerobic organisms, cellular respiration occurs in the presence of oxygen, leading to the efficient production of ATP. In anaerobic organisms, cellular respiration occurs in the absence of oxygen, resulting in less ATP production and the accumulation of lactic acid or ethanol as byproducts.\n",
      "\n",
      "5. Can you explain how the electron transport chain is involved in cellular respiration?\n",
      "   - The electron transport chain is a series of protein complexes and molecules located in the inner mitochondrial membrane. During cellular respiration, it plays a crucial role in generating ATP by transferring electrons from NADH and FADH2 to oxygen, ultimately forming water and producing a proton gradient that drives ATP synthesis.\n",
      "\n",
      "#### Application-based:\n",
      "6. If a person is engaged in intense physical activity, how does their body adjust its cellular respiration process to meet the increased demand for ATP?\n",
      "   - During intense physical activity, the body increases its rate of cellular respiration to meet the higher demand for ATP. This involves a higher consumption of oxygen and increased production of ATP through aerobic metabolism.\n",
      "\n",
      "7. How might a malfunction in the cellular respiration process impact an organism's overall health and energy levels?\n",
      "   - A malfunction in cellular respiration can lead to decreased ATP production, which can result in reduced energy levels, fatigue, and various health issues. For example, mitochondrial disorders are often associated with impaired cellular respiration and can manifest as muscle weakness, neurological problems, and other symptoms.\n",
      "\n",
      "8. How could environmental factors such as temperature or oxygen availability influence cellular respiration in organisms?\n",
      "   - Environmental factors like temperature and oxygen availability can significantly impact the efficiency of cellular respiration. High temperatures can denature enzymes involved in the process, while low temperatures can slow down enzyme activity. Oxygen availability directly affects aerobic respiration; low oxygen levels can force cells to rely more on anaerobic pathways, leading to less efficient ATP production and potential accumulation of harmful byproducts like lactic acid.\n",
      "\n",
      "Iteration 4:\n",
      "Current prompt: Generate a set of relevant questions and their corresponding answers about the given text, covering factual, analytical, and application-based types to provide both surface-level and in-depth knowledge of the subject. Include synthesis of information from multiple sources for analytical questions and novel situations or problem-solving for application-based questions. Ensure technical details are precise and detailed, and include a broader range of related topics to deepen understanding and critical thinking. Address cases where the answer is not directly found in the text by integrating general scientific knowledge or indicating where assumptions are being made.\n",
      "Feedback: ### Feedback on the Prompt and Results\n",
      "\n",
      "#### 1. Clarity of the Prompt\n",
      "The prompt is clear and well-structured, providing detailed instructions on generating questions and answers that cover factual, analytical, and application-based types. It also specifies the need to include technical details and a broader range of related topics.\n",
      "\n",
      "#### 2. Accuracy of the Generated Answers\n",
      "- **Factual Questions:**\n",
      "  1. The primary purpose of cellular respiration is accurately described.\n",
      "  2. The waste products (carbon dioxide and water) are correctly identified.\n",
      "\n",
      "- **Analytical Questions:**\n",
      "  3. The explanation of ATP generation is mostly accurate but could benefit from more detail, especially about glycolysis, the citric acid cycle, and the electron transport chain.\n",
      "  4. The distinction between aerobic and anaerobic conditions is correct, but the answer could mention specific pathways like glycolysis, fermentation, and oxidative phosphorylation.\n",
      "\n",
      "- **Application-based Questions:**\n",
      "  5. The impact of exercise on cellular respiration is well-explained, noting the increased demand for ATP and oxygen consumption.\n",
      "  6. The effect of diabetes on cellular respiration is correctly identified, mentioning impaired glucose metabolism and its consequences.\n",
      "\n",
      "#### 3. Handling of Cases Where the Answer is Not in the Text\n",
      "- The generated answers effectively handle information not explicitly stated in the provided text by incorporating general scientific knowledge.\n",
      "- For instance, the explanation of how cellular respiration generates ATP and the impact of diseases like diabetes involves synthesizing information that wasn't directly provided but is consistent with general biological principles.\n",
      "\n",
      "#### 4. Suggestions for Improvement\n",
      "- **Detail Enhancement:**\n",
      "  - For analytical questions, provide more detailed steps of the processes involved. For example, mention glycolysis, the citric acid cycle, and the electron transport chain specifically when discussing ATP generation.\n",
      "  - When discussing differences in aerobic and anaerobic respiration, include the specific byproducts of anaerobic respiration in muscle cells (lactic acid) and yeast cells (ethanol and carbon dioxide).\n",
      "\n",
      "- **Broader Range of Topics:**\n",
      "  - Include questions that link cellular respiration to other physiological systems or processes, such as the role of respiratory and circulatory systems in delivering oxygen to cells.\n",
      "  - Consider questions on the evolutionary significance of cellular respiration or its variations among different organisms.\n",
      "\n",
      "- **Critical Thinking and Synthesis:**\n",
      "  - For application-based questions, pose scenarios or problems that require the application of cellular respiration knowledge to novel situations, such as the impact of high-altitude environments on cellular respiration.\n",
      "  - Encourage critical thinking by asking about potential therapeutic approaches to mitigate the impact of diseases like diabetes on cellular respiration.\n",
      "\n",
      "### Revised Questions and Answers\n",
      "- **Analytical:**\n",
      "  3. How is ATP generated during cellular respiration?\n",
      "     - Answer: ATP is generated during cellular respiration through a series of metabolic processes including glycolysis, the citric acid cycle (Krebs cycle), and the electron transport chain. Glycolysis occurs in the cytoplasm, breaking down glucose into pyruvate while producing a small amount of ATP and NADH. The pyruvate enters the mitochondria and is further processed in the citric acid cycle, generating more NADH and FADH2. These high-energy molecules then donate electrons to the electron transport chain in the inner mitochondrial membrane, driving the production of a large amount of ATP through oxidative phosphorylation.\n",
      "\n",
      "- **Application-based:**\n",
      "  6. How do certain diseases, such as diabetes, affect cellular respiration?\n",
      "     - Answer: In diabetes, impaired glucose metabolism can affect cellular respiration by disrupting the process of converting glucose into ATP. In Type 1 diabetes, the lack of insulin means glucose cannot enter cells effectively, leading to reduced ATP production. In Type 2 diabetes, insulin resistance results in inefficient glucose uptake. Both conditions can cause cells to rely more on fat metabolism, leading to the production of ketone bodies and potential energy imbalances in cells and tissues, impacting overall metabolic function.\n",
      "\n",
      "These adjustments should enhance the depth and accuracy of the responses, promoting a more comprehensive understanding of cellular respiration.\n",
      "Cleaned prompt: Generate a set of relevant questions and their corresponding answers about the given text, covering factual, analytical, and application-based types. Include synthesis of information from multiple sources for analytical questions and novel situations or problem-solving for application-based questions. Ensure technical details are precise and detailed, and include a broader range of related topics to deepen understanding and critical thinking. Address cases where the answer is not directly found in the text by integrating general scientific knowledge or indicating where assumptions are being made.\n",
      "Updated value: Generate a set of relevant questions and their corresponding answers about the given text, covering factual, analytical, and application-based types. Include synthesis of information from multiple sources for analytical questions and novel situations or problem-solving for application-based questions. Ensure technical details are precise and detailed, and include a broader range of related topics to deepen understanding and critical thinking. Address cases where the answer is not directly found in the text by integrating general scientific knowledge or indicating where assumptions are being made.\n",
      "Feedback: ### Feedback on the Prompt and Results\n",
      "\n",
      "#### 1. Clarity of the Prompt\n",
      "The prompt is clear and well-structured, providing detailed instructions on generating questions and answers that cover factual, analytical, and application-based types. It also specifies the need to include technical details and a broader range of related topics.\n",
      "\n",
      "#### 2. Accuracy of the Generated Answers\n",
      "- **Factual Questions:**\n",
      "  1. The primary purpose of cellular respiration is accurately described.\n",
      "  2. The waste products (carbon dioxide and water) are correctly identified.\n",
      "\n",
      "- **Analytical Questions:**\n",
      "  3. The explanation of ATP generation is mostly accurate but could benefit from more detail, especially about glycolysis, the citric acid cycle, and the electron transport chain.\n",
      "  4. The distinction between aerobic and anaerobic conditions is correct, but the answer could mention specific pathways like glycolysis, fermentation, and oxidative phosphorylation.\n",
      "\n",
      "- **Application-based Questions:**\n",
      "  5. The impact of exercise on cellular respiration is well-explained, noting the increased demand for ATP and oxygen consumption.\n",
      "  6. The effect of diabetes on cellular respiration is correctly identified, mentioning impaired glucose metabolism and its consequences.\n",
      "\n",
      "#### 3. Handling of Cases Where the Answer is Not in the Text\n",
      "- The generated answers effectively handle information not explicitly stated in the provided text by incorporating general scientific knowledge.\n",
      "- For instance, the explanation of how cellular respiration generates ATP and the impact of diseases like diabetes involves synthesizing information that wasn't directly provided but is consistent with general biological principles.\n",
      "\n",
      "#### 4. Suggestions for Improvement\n",
      "- **Detail Enhancement:**\n",
      "  - For analytical questions, provide more detailed steps of the processes involved. For example, mention glycolysis, the citric acid cycle, and the electron transport chain specifically when discussing ATP generation.\n",
      "  - When discussing differences in aerobic and anaerobic respiration, include the specific byproducts of anaerobic respiration in muscle cells (lactic acid) and yeast cells (ethanol and carbon dioxide).\n",
      "\n",
      "- **Broader Range of Topics:**\n",
      "  - Include questions that link cellular respiration to other physiological systems or processes, such as the role of respiratory and circulatory systems in delivering oxygen to cells.\n",
      "  - Consider questions on the evolutionary significance of cellular respiration or its variations among different organisms.\n",
      "\n",
      "- **Critical Thinking and Synthesis:**\n",
      "  - For application-based questions, pose scenarios or problems that require the application of cellular respiration knowledge to novel situations, such as the impact of high-altitude environments on cellular respiration.\n",
      "  - Encourage critical thinking by asking about potential therapeutic approaches to mitigate the impact of diseases like diabetes on cellular respiration.\n",
      "\n",
      "### Revised Questions and Answers\n",
      "- **Analytical:**\n",
      "  3. How is ATP generated during cellular respiration?\n",
      "     - Answer: ATP is generated during cellular respiration through a series of metabolic processes including glycolysis, the citric acid cycle (Krebs cycle), and the electron transport chain. Glycolysis occurs in the cytoplasm, breaking down glucose into pyruvate while producing a small amount of ATP and NADH. The pyruvate enters the mitochondria and is further processed in the citric acid cycle, generating more NADH and FADH2. These high-energy molecules then donate electrons to the electron transport chain in the inner mitochondrial membrane, driving the production of a large amount of ATP through oxidative phosphorylation.\n",
      "\n",
      "- **Application-based:**\n",
      "  6. How do certain diseases, such as diabetes, affect cellular respiration?\n",
      "     - Answer: In diabetes, impaired glucose metabolism can affect cellular respiration by disrupting the process of converting glucose into ATP. In Type 1 diabetes, the lack of insulin means glucose cannot enter cells effectively, leading to reduced ATP production. In Type 2 diabetes, insulin resistance results in inefficient glucose uptake. Both conditions can cause cells to rely more on fat metabolism, leading to the production of ketone bodies and potential energy imbalances in cells and tissues, impacting overall metabolic function.\n",
      "\n",
      "These adjustments should enhance the depth and accuracy of the responses, promoting a more comprehensive understanding of cellular respiration.\n",
      "\n",
      "Iteration 5:\n",
      "Current prompt: Generate a set of relevant questions and their corresponding answers about the given text, covering factual, analytical, and application-based types. Include synthesis of information from multiple sources for analytical questions and novel situations or problem-solving for application-based questions. Ensure technical details are precise and detailed, and include a broader range of related topics to deepen understanding and critical thinking. Address cases where the answer is not directly found in the text by integrating general scientific knowledge or indicating where assumptions are being made.\n",
      "Feedback: ### Feedback\n",
      "\n",
      "#### 1. Clarity of the Prompt\n",
      "- **Strengths:** The prompt is detailed and clear in its expectations. It specifies the need for factual, analytical, and application-based questions, and emphasizes the importance of integrating information from multiple sources and providing precise technical details.\n",
      "- **Areas for Improvement:** The prompt could benefit from an example or two to illustrate the difference between the types of questions expected (factual, analytical, application-based). This would help ensure that the generated questions meet the intended criteria.\n",
      "\n",
      "#### 2. Accuracy of the Generated Answers\n",
      "- **Strengths:** The answers provided are accurate based on the given text and general scientific knowledge. The explanations for the stages of cellular respiration and the role of oxygen are clear and correct.\n",
      "- **Areas for Improvement:** The answer to question 6 could be expanded to mention specific types of fermentation (e.g., lactic acid fermentation and alcoholic fermentation) to provide more depth.\n",
      "\n",
      "#### 3. Handling of Cases Where the Answer is Not in the Text\n",
      "- **Strengths:** The generated answers appropriately integrate general scientific knowledge when the answer is not directly found in the text. For example, the details about photosynthesis and the comparison between aerobic and anaerobic respiration are well-handled.\n",
      "- **Areas for Improvement:** It would be beneficial to explicitly state when assumptions are being made or when additional scientific knowledge is being integrated. For instance, the answer to question 2 could mention that the comparison with photosynthesis is based on general scientific understanding rather than the provided text.\n",
      "\n",
      "#### 4. Suggestions for Improvement\n",
      "- **Factual Questions:** Ensure that all factual questions directly address information explicitly stated in the text. The current set does this well, but adding a question about the specific waste products mentioned (e.g., \"What waste products are released during cellular respiration?\") confirms comprehension.\n",
      "- **Analytical Questions:** Develop questions that require synthesis of information from multiple sources or deeper analysis. For example, \"How might the efficiency of cellular respiration be affected by varying levels of oxygen availability?\" This requires understanding and integrating knowledge about aerobic and anaerobic respiration.\n",
      "- **Application-Based Questions:** Include more questions that require applying the knowledge to novel situations or problem-solving. For example, \"How might a mutation that affects the electron transport chain impact ATP production in cells?\"\n",
      "- **Explicit Assumptions:** Clearly indicate when the answer is based on assumptions or general knowledge not provided in the text. This transparency helps differentiate between text-based and inferential knowledge.\n",
      "\n",
      "Overall, the results are solid and demonstrate a good understanding of cellular respiration, but there is room for adding more depth and critical thinking elements to meet the full scope of the prompt.\n",
      "Cleaned prompt: Generate a set of relevant questions and their corresponding answers about the given text, covering factual, analytical, and application-based types. Include synthesis of information from multiple sources for analytical questions and novel situations or problem-solving for application-based questions. Ensure technical details are precise and detailed, and include a broader range of related topics to deepen understanding and critical thinking. Address cases where the answer is not directly found in the text by integrating general scientific knowledge or indicating where assumptions are being made.\n",
      "Updated value: Generate a set of relevant questions and their corresponding answers about the given text, covering factual, analytical, and application-based types. Include synthesis of information from multiple sources for analytical questions and novel situations or problem-solving for application-based questions. Ensure technical details are precise and detailed, and include a broader range of related topics to deepen understanding and critical thinking. Address cases where the answer is not directly found in the text by integrating general scientific knowledge or indicating where assumptions are being made.\n",
      "Feedback: ### Feedback\n",
      "\n",
      "#### 1. Clarity of the Prompt\n",
      "- **Strengths:** The prompt is detailed and clear in its expectations. It specifies the need for factual, analytical, and application-based questions, and emphasizes the importance of integrating information from multiple sources and providing precise technical details.\n",
      "- **Areas for Improvement:** The prompt could benefit from an example or two to illustrate the difference between the types of questions expected (factual, analytical, application-based). This would help ensure that the generated questions meet the intended criteria.\n",
      "\n",
      "#### 2. Accuracy of the Generated Answers\n",
      "- **Strengths:** The answers provided are accurate based on the given text and general scientific knowledge. The explanations for the stages of cellular respiration and the role of oxygen are clear and correct.\n",
      "- **Areas for Improvement:** The answer to question 6 could be expanded to mention specific types of fermentation (e.g., lactic acid fermentation and alcoholic fermentation) to provide more depth.\n",
      "\n",
      "#### 3. Handling of Cases Where the Answer is Not in the Text\n",
      "- **Strengths:** The generated answers appropriately integrate general scientific knowledge when the answer is not directly found in the text. For example, the details about photosynthesis and the comparison between aerobic and anaerobic respiration are well-handled.\n",
      "- **Areas for Improvement:** It would be beneficial to explicitly state when assumptions are being made or when additional scientific knowledge is being integrated. For instance, the answer to question 2 could mention that the comparison with photosynthesis is based on general scientific understanding rather than the provided text.\n",
      "\n",
      "#### 4. Suggestions for Improvement\n",
      "- **Factual Questions:** Ensure that all factual questions directly address information explicitly stated in the text. The current set does this well, but adding a question about the specific waste products mentioned (e.g., \"What waste products are released during cellular respiration?\") confirms comprehension.\n",
      "- **Analytical Questions:** Develop questions that require synthesis of information from multiple sources or deeper analysis. For example, \"How might the efficiency of cellular respiration be affected by varying levels of oxygen availability?\" This requires understanding and integrating knowledge about aerobic and anaerobic respiration.\n",
      "- **Application-Based Questions:** Include more questions that require applying the knowledge to novel situations or problem-solving. For example, \"How might a mutation that affects the electron transport chain impact ATP production in cells?\"\n",
      "- **Explicit Assumptions:** Clearly indicate when the answer is based on assumptions or general knowledge not provided in the text. This transparency helps differentiate between text-based and inferential knowledge.\n",
      "\n",
      "Overall, the results are solid and demonstrate a good understanding of cellular respiration, but there is room for adding more depth and critical thinking elements to meet the full scope of the prompt.\n",
      "\n",
      "\n",
      "\n",
      "Final optimized GENERATE_QA_PAIRS_PROMPT:\n",
      "OptimizationResult(optimized_prompt='Generate a set of relevant questions and their corresponding answers about the given text, covering factual, analytical, and application-based types. Include synthesis of information from multiple sources for analytical questions and novel situations or problem-solving for application-based questions. Ensure technical details are precise and detailed, and include a broader range of related topics to deepen understanding and critical thinking. Address cases where the answer is not directly found in the text by integrating general scientific knowledge or indicating where assumptions are being made.', model='gpt-4o-mini', temperature=0.7, top_p=0.95, frequency_penalty=0)\n"
     ]
    }
   ],
   "source": [
    "initial_prompt = \"\"\"\n",
    "Generate a set of relevant questions and their\n",
    "corresponding answers about the given text. Ensure the\n",
    "questions cover a mix of factual, analytical, and\n",
    "application-based types to provide both surface-level\n",
    "and in-depth knowledge of the subject.\n",
    "\"\"\"\n",
    "\n",
    "data = [\n",
    "  \"The process of cellular respiration converts biochemical energy from nutrients into adenosine triphosphate (ATP), and releases waste products. Cellular respiration is a set of metabolic reactions and processes that take place in the cells of organisms to convert chemical energy from oxygen molecules or nutrients into ATP, and then release waste products.\",\n",
    "  \"The first human heart transplant was performed by Dr. Christiaan Barnard on December 3, 1967, in Cape Town, South Africa. The patient, Louis Washkansky, lived for 18 days after the surgery.\",\n",
    "  \"Rosalind Franklin was a British biophysicist and X-ray crystallographer whose work was critical in the understanding of the molecular structures of DNA, RNA, and viruses. She is best known for her photograph of DNA, known as Photo 51, which contributed significantly to the discovery of the DNA double helix by Watson and Crick.\",\n",
    "  \"The Great Wall of China is a series of fortifications made of various materials, including stone, brick, tamped earth, wood, and other materials. It was built along the northern borders of China to protect against invasions and raids from various nomadic groups. The most well-known sections were built by the Ming Dynasty (1368-1644).\",\n",
    "  \"The Theory of General Relativity, formulated by Albert Einstein, describes the gravitational force as a curvature of spacetime caused by mass and energy. One of its most famous predictions is the bending of light around massive objects, which has been confirmed through various experiments, including the observation of a solar eclipse in 1919.\",\n",
    "  \"Marie Curie was a Polish-born physicist and chemist who conducted pioneering research on radioactivity. She was the first woman to win a Nobel Prize and the only person to win Nobel Prizes in two different scientific fields—Physics (1903) and Chemistry (1911). Her discoveries included the elements polonium and radium.\",\n",
    "  \"The internet is a global network of interconnected computers that communicate via standardized protocols. It enables a wide range of services, including the World Wide Web, email, and file sharing. The internet has revolutionized communication, commerce, and access to information.\",\n",
    "]\n",
    "inputs = data[:1]\n",
    "\n",
    "result = optimize_prompt(\n",
    "  initial_prompt,\n",
    "  \"gpt-4o-mini\",\n",
    "  \"gpt-4o\",\n",
    "  inputs,\n",
    ")\n",
    "\n",
    "print(\n",
    "  f\"\\n\\nFinal optimized GENERATE_QA_PAIRS_PROMPT:\\n{result}\"\n",
    ")\n",
    "GENERATE_QA_PAIRS_PROMPT = result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prompt: Given a question, generate rewordings of the question.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_prompt = \"\"\"\n",
    "Rephrase the given question in at least three distinct ways while maintaining\n",
    "the original meaning and context. Aim for creativity, avoid repetition, and\n",
    "steer clear of overly complex vocabulary.\n",
    "\"\"\"\n",
    "\n",
    "data = [\n",
    "  \"What is ATP and why is it important?\",\n",
    "  \"What are the main themes explored in George Orwell's novel '1984'?\",\n",
    "  \"How does the process of photosynthesis benefit plant life and ecosystems?\",\n",
    "  \"What were the primary causes of the fall of the Roman Empire?\",\n",
    "  \"What role does the Federal Reserve play in the United States economy?\",\n",
    "  \"How does Quantum Computing differ from Classical Computing?\",\n",
    "  \"What are the ethical implications of genetic engineering in humans?\",\n",
    "  \"How do vaccines work to prevent diseases at the molecular level?\",\n",
    "  \"What architectural features are characteristic of Gothic cathedrals?\",\n",
    "  \"What is the significance of the Theory of Relativity in modern physics?\",\n",
    "]\n",
    "inputs = data[:1]\n",
    "\n",
    "result = optimize_prompt(\n",
    "  initial_prompt,\n",
    "  \"gpt-4o-mini\",\n",
    "  \"gpt-4o\",\n",
    "  inputs,\n",
    ")\n",
    "\n",
    "print(\n",
    "  f\"\\n\\nFinal optimized REWORDING_PROMPT:\\n{result}\"\n",
    ")\n",
    "REWORDING_PROMPT = result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "TEXT_CONTAINS_ANSWER_PROMPT = OptimizationResult(\n",
    "  optimized_prompt=dedent(\"\"\"\n",
    "Does the provided text explicitly contain the answer\n",
    "to the given question? Respond with \"Yes\" or \"No\" and\n",
    "provide a brief explanation.\n",
    "\"\"\").strip(),\n",
    "  model=\"gpt-4o-mini\",\n",
    "  temperature=0.7,\n",
    "  top_p=0.95,\n",
    "  frequency_penalty=0,\n",
    ")\n",
    "\n",
    "\n",
    "GENERATE_QA_PAIRS_PROMPT = OptimizationResult(\n",
    "  optimized_prompt=dedent(\"\"\"\n",
    "Generate relevant questions and their corresponding\n",
    "answers about the given text, covering factual,\n",
    "analytical, and application-based types. Ensure all\n",
    "questions and answers are derived strictly from the\n",
    "text without introducing external information.\n",
    "\"\"\").strip(),\n",
    "  model=\"gpt-4o-mini\",\n",
    "  temperature=0.7,\n",
    "  top_p=0.95,\n",
    "  frequency_penalty=0,\n",
    ")\n",
    "\n",
    "\n",
    "REWORD_QUESTION_PROMPT = OptimizationResult(\n",
    "  optimized_prompt=dedent(\"\"\"\n",
    "Rephrase the following question in three distinct\n",
    "ways, ensuring the original meaning and context remain\n",
    "intact. Each version should be structurally unique and\n",
    "use clear language.\n",
    "\"\"\").strip(),\n",
    "  model=\"gpt-4o-mini\",\n",
    "  temperature=0.7,\n",
    "  top_p=0.95,\n",
    "  frequency_penalty=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the optimized prompts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "#\n",
    "\n",
    "\n",
    "def generate_qa_pairs(\n",
    "  text: str,\n",
    "  model: str = \"gpt-4o-mini\",\n",
    ") -> dict:\n",
    "  json_prompt = dedent(\"\"\"\n",
    "Extract a JSON list of question-answer pairsfrom the given text. Each pair should\n",
    "include a question (Q) and its corresponding answer (A).\n",
    "                         \"\"\").strip()\n",
    "\n",
    "  result = call_llm_with_json_output(\n",
    "    prompt=GENERATE_QA_PAIRS_PROMPT.optimized_prompt,\n",
    "    json_prompt=json_prompt,\n",
    "    input=text,\n",
    "    model=GENERATE_QA_PAIRS_PROMPT.model,\n",
    "    temperature=GENERATE_QA_PAIRS_PROMPT.temperature,\n",
    "    max_tokens=2048,\n",
    "    top_p=GENERATE_QA_PAIRS_PROMPT.top_p,\n",
    "    frequency_penalty=GENERATE_QA_PAIRS_PROMPT.frequency_penalty,\n",
    "  )\n",
    "  print(f\"Result for text:\\n{text}\\n{result}\")\n",
    "  return json.loads(result)\n",
    "\n",
    "\n",
    "def reword_question(\n",
    "  question: str,\n",
    "  model: str = \"gpt-4o-mini\",\n",
    ") -> dict:\n",
    "  json_prompt = dedent(\"\"\"\n",
    "Extract a JSON list of questions from the given text (i.e., an array of strings).\n",
    "                         \"\"\").strip()\n",
    "  result = call_llm_with_json_output(\n",
    "    prompt=REWORD_QUESTION_PROMPT.optimized_prompt,\n",
    "    json_prompt=json_prompt,\n",
    "    input=question,\n",
    "    model=REWORD_QUESTION_PROMPT.model,\n",
    "    temperature=REWORD_QUESTION_PROMPT.temperature,\n",
    "    max_tokens=2048,\n",
    "    top_p=REWORD_QUESTION_PROMPT.top_p,\n",
    "    frequency_penalty=REWORD_QUESTION_PROMPT.frequency_penalty,\n",
    "  )\n",
    "  print(f\"Result for question:\\n{question}\\n{result}\")\n",
    "  return json.loads(result)\n",
    "\n",
    "\n",
    "def expand_questions_for_qa_pairs(\n",
    "  qa_pairs: dict,\n",
    ") -> list:\n",
    "  qa_rewrites = []\n",
    "  pairs = get_json_list(qa_pairs)\n",
    "  for pair in pairs:\n",
    "    question = pair[\"Q\"]\n",
    "    print(f\"Original Question: {question}\")\n",
    "    reworded_questions_obj = reword_question(question)\n",
    "    reworded_questions = get_json_list(\n",
    "      reworded_questions_obj\n",
    "    )\n",
    "    print(\"Reworded Questions:\")\n",
    "    for reworded_question in reworded_questions:\n",
    "      print(\"\\t\", reworded_question)\n",
    "    qa_rewrites.append(\n",
    "      {\n",
    "        \"Q\": [question] + reworded_questions,\n",
    "        \"A\": pair[\"A\"],\n",
    "      }\n",
    "    )\n",
    "    print(\"\\n\")\n",
    "  return qa_rewrites\n",
    "\n",
    "\n",
    "def generate_text_expanded_qa(\n",
    "  text: str,\n",
    ") -> list[dict]:\n",
    "  qa_pairs = generate_qa_pairs(text)\n",
    "  expanded_qa_pairs = expand_questions_for_qa_pairs(\n",
    "    qa_pairs\n",
    "  )\n",
    "  return expanded_qa_pairs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial result: 1. What is the concept of open-endedness in the context of generating data from systems?\n",
      "- The concept of open-endedness involves studying systems that can generate their own data in an infinite capacity, becoming more complex and producing increasingly interesting data when run for longer periods.\n",
      "\n",
      "2. How can self-improving systems that generate interesting data be beneficial in training models?\n",
      "- Self-improving systems that generate interesting data can be used to further train models, enhancing their learning capabilities and performance.\n",
      "\n",
      "3. What challenge is highlighted in the text regarding creating a perpetual data machine?\n",
      "- The challenge highlighted is how to generate net new information from a model trained on previous data when aiming to create a perpetual data machine.\n",
      "\n",
      "4. How does the idea of a perpetual data machine raise questions about generating new information?\n",
      "- The idea of a perpetual data machine raises questions about how to continuously generate new and meaningful information from a model that has been trained on existing data, to avoid stagnation or repetition.\n",
      "Result for text:\n",
      "\n",
      "Open-endedness is essentially studying systems that can generate their own data\n",
      "in an infinite capacity. These systems, if run for longer periods, become more\n",
      "complex and generate increasingly interesting data. If we can crack the\n",
      "challenge of creating a self-improving system that keeps generating interesting\n",
      "data, we can use that data to further train our models. However, this leads to\n",
      "the idea of a perpetual data machine, raising the question of how to generate\n",
      "net new information from a model trained on previous data.\n",
      "\n",
      "{\n",
      "\t\"pairs\": [{\n",
      "\t\t\t\"Q\": \"What is the concept of open-endedness in the context of generating data from systems?\",\n",
      "\t\t\t\"A\": \"The concept of open-endedness involves studying systems that can generate their own data in an infinite capacity, becoming more complex and producing increasingly interesting data when run for longer periods.\"\n",
      "\t\t},\n",
      "\t\t{\n",
      "\t\t\t\"Q\": \"How can self-improving systems that generate interesting data be beneficial in training models?\",\n",
      "\t\t\t\"A\": \"Self-improving systems that generate interesting data can be used to further train models, enhancing their learning capabilities and performance.\"\n",
      "\t\t},\n",
      "\t\t{\n",
      "\t\t\t\"Q\": \"What challenge is highlighted in the text regarding creating a perpetual data machine?\",\n",
      "\t\t\t\"A\": \"The challenge highlighted is how to generate net new information from a model trained on previous data when aiming to create a perpetual data machine.\"\n",
      "\t\t},\n",
      "\t\t{\n",
      "\t\t\t\"Q\": \"How does the idea of a perpetual data machine raise questions about generating new information?\",\n",
      "\t\t\t\"A\": \"The idea of a perpetual data machine raises questions about how to continuously generate new and meaningful information from a model that has been trained on existing data, to avoid stagnation or repetition.\"\n",
      "\t\t}\n",
      "\t]\n",
      "}\n",
      "Original Question: What is the concept of open-endedness in the context of generating data from systems?\n",
      "Initial result: 1. How is the idea of open-endedness defined when it comes to producing data from systems?\n",
      "\n",
      "2. Can you explain the concept of open-endedness within the framework of data generation from systems?\n",
      "\n",
      "3. In generating data from systems, how is the concept of open-endedness understood and applied?\n",
      "Result for question:\n",
      "What is the concept of open-endedness in the context of generating data from systems?\n",
      "{\n",
      "  \"questions\": [\n",
      "    \"How is the idea of open-endedness defined when it comes to producing data from systems?\",\n",
      "    \"Can you explain the concept of open-endedness within the framework of data generation from systems?\",\n",
      "    \"In generating data from systems, how is the concept of open-endedness understood and applied?\"\n",
      "  ]\n",
      "}\n",
      "Reworded Questions:\n",
      "\t How is the idea of open-endedness defined when it comes to producing data from systems?\n",
      "\t Can you explain the concept of open-endedness within the framework of data generation from systems?\n",
      "\t In generating data from systems, how is the concept of open-endedness understood and applied?\n",
      "\n",
      "\n",
      "Original Question: How can self-improving systems that generate interesting data be beneficial in training models?\n",
      "Initial result: 1. What are the advantages of utilizing self-enhancing systems that produce intriguing data for training models?\n",
      "\n",
      "2. In what ways can self-improving systems that generate compelling data contribute to the training of models?\n",
      "\n",
      "3. How might the use of self-improving systems that generate engaging data prove advantageous in the training of models?\n",
      "Result for question:\n",
      "How can self-improving systems that generate interesting data be beneficial in training models?\n",
      "{\n",
      "  \"questions\": [\n",
      "    \"What are the advantages of utilizing self-enhancing systems that produce intriguing data for training models?\",\n",
      "    \"In what ways can self-improving systems that generate compelling data contribute to the training of models?\",\n",
      "    \"How might the use of self-improving systems that generate engaging data prove advantageous in the training of models?\"\n",
      "  ]\n",
      "}\n",
      "Reworded Questions:\n",
      "\t What are the advantages of utilizing self-enhancing systems that produce intriguing data for training models?\n",
      "\t In what ways can self-improving systems that generate compelling data contribute to the training of models?\n",
      "\t How might the use of self-improving systems that generate engaging data prove advantageous in the training of models?\n",
      "\n",
      "\n",
      "Original Question: What challenge is highlighted in the text regarding creating a perpetual data machine?\n",
      "Initial result: - In the text, what difficulty is emphasized when it comes to developing a perpetual data machine?\n",
      "- What obstacle is pointed out in the text about constructing a perpetual data machine?\n",
      "- What problem is brought to attention in the text regarding the creation of a perpetual data machine?\n",
      "Result for question:\n",
      "What challenge is highlighted in the text regarding creating a perpetual data machine?\n",
      "{\n",
      "  \"questions\": [\n",
      "    \"What difficulty is emphasized when it comes to developing a perpetual data machine?\",\n",
      "    \"What obstacle is pointed out in the text about constructing a perpetual data machine?\",\n",
      "    \"What problem is brought to attention in the text regarding the creation of a perpetual data machine?\"\n",
      "  ]\n",
      "}\n",
      "Reworded Questions:\n",
      "\t What difficulty is emphasized when it comes to developing a perpetual data machine?\n",
      "\t What obstacle is pointed out in the text about constructing a perpetual data machine?\n",
      "\t What problem is brought to attention in the text regarding the creation of a perpetual data machine?\n",
      "\n",
      "\n",
      "Original Question: How does the idea of a perpetual data machine raise questions about generating new information?\n",
      "Initial result: 1. In what ways does the concept of a perpetual data machine prompt inquiries into the creation of fresh information?\n",
      "\n",
      "2. What kinds of questions are sparked regarding the generation of new information by the notion of a perpetual data machine?\n",
      "\n",
      "3. How does the notion of a perpetual data machine lead to discussions about producing novel information?\n",
      "Result for question:\n",
      "How does the idea of a perpetual data machine raise questions about generating new information?\n",
      "{\n",
      "  \"questions\": [\n",
      "    \"In what ways does the concept of a perpetual data machine prompt inquiries into the creation of fresh information?\",\n",
      "    \"What kinds of questions are sparked regarding the generation of new information by the notion of a perpetual data machine?\",\n",
      "    \"How does the notion of a perpetual data machine lead to discussions about producing novel information?\"\n",
      "  ]\n",
      "}\n",
      "Reworded Questions:\n",
      "\t In what ways does the concept of a perpetual data machine prompt inquiries into the creation of fresh information?\n",
      "\t What kinds of questions are sparked regarding the generation of new information by the notion of a perpetual data machine?\n",
      "\t How does the notion of a perpetual data machine lead to discussions about producing novel information?\n",
      "\n",
      "\n",
      "[\n",
      "  {\n",
      "    \"Q\": [\n",
      "      \"What is the concept of open-endedness in the context of generating data from systems?\",\n",
      "      \"How is the idea of open-endedness defined when it comes to producing data from systems?\",\n",
      "      \"Can you explain the concept of open-endedness within the framework of data generation from systems?\",\n",
      "      \"In generating data from systems, how is the concept of open-endedness understood and applied?\"\n",
      "    ],\n",
      "    \"A\": \"The concept of open-endedness involves studying systems that can generate their own data in an infinite capacity, becoming more complex and producing increasingly interesting data when run for longer periods.\"\n",
      "  },\n",
      "  {\n",
      "    \"Q\": [\n",
      "      \"How can self-improving systems that generate interesting data be beneficial in training models?\",\n",
      "      \"What are the advantages of utilizing self-enhancing systems that produce intriguing data for training models?\",\n",
      "      \"In what ways can self-improving systems that generate compelling data contribute to the training of models?\",\n",
      "      \"How might the use of self-improving systems that generate engaging data prove advantageous in the training of models?\"\n",
      "    ],\n",
      "    \"A\": \"Self-improving systems that generate interesting data can be used to further train models, enhancing their learning capabilities and performance.\"\n",
      "  },\n",
      "  {\n",
      "    \"Q\": [\n",
      "      \"What challenge is highlighted in the text regarding creating a perpetual data machine?\",\n",
      "      \"What difficulty is emphasized when it comes to developing a perpetual data machine?\",\n",
      "      \"What obstacle is pointed out in the text about constructing a perpetual data machine?\",\n",
      "      \"What problem is brought to attention in the text regarding the creation of a perpetual data machine?\"\n",
      "    ],\n",
      "    \"A\": \"The challenge highlighted is how to generate net new information from a model trained on previous data when aiming to create a perpetual data machine.\"\n",
      "  },\n",
      "  {\n",
      "    \"Q\": [\n",
      "      \"How does the idea of a perpetual data machine raise questions about generating new information?\",\n",
      "      \"In what ways does the concept of a perpetual data machine prompt inquiries into the creation of fresh information?\",\n",
      "      \"What kinds of questions are sparked regarding the generation of new information by the notion of a perpetual data machine?\",\n",
      "      \"How does the notion of a perpetual data machine lead to discussions about producing novel information?\"\n",
      "    ],\n",
      "    \"A\": \"The idea of a perpetual data machine raises questions about how to continuously generate new and meaningful information from a model that has been trained on existing data, to avoid stagnation or repetition.\"\n",
      "  }\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "text = \"\"\"\n",
    "Open-endedness is essentially studying systems that can generate their own data\n",
    "in an infinite capacity. These systems, if run for longer periods, become more\n",
    "complex and generate increasingly interesting data. If we can crack the\n",
    "challenge of creating a self-improving system that keeps generating interesting\n",
    "data, we can use that data to further train our models. However, this leads to\n",
    "the idea of a perpetual data machine, raising the question of how to generate\n",
    "net new information from a model trained on previous data.\n",
    "\"\"\"\n",
    "qa_pairs = generate_text_expanded_qa(text)\n",
    "print(json.dumps(qa_pairs, indent=2))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
